6.2.2 Proportion of the top 5 communities in terms of citations and authors
-
-
-Code
-
#|label: gt
-# Sum of all citations from filtered_df for each community of the top 5
-top5_communities_citations <- filtered_df %>%
-group_by(community) %>%
- dplyr::summarise(citations =sum(citations)) %>%
-arrange(desc(citations))
-
-sum_citations_df_1996_2023 <-sum(df_1996_2023$citations)
-
-# Compute the proportion of citations and number of authors for each community
-top5_communities_citations <- top5_communities_citations %>%
-left_join(top5_communities, by ="community") %>%
- dplyr::mutate(prop_citations = citations / sum_citations_df_1996_2023) %>%
- dplyr::mutate(prop_authors = n /nrow(df_1996_2023)) %>%
- dplyr::mutate(cumul_prop_citations =round(cumsum(prop_citations) *100,2)) %>%
- dplyr::mutate(cumul_prop_authors =round(cumsum(prop_authors) *100,2)) %>%
- dplyr::mutate(prop_citations =round(prop_citations *100,2)) %>%
- dplyr::mutate(prop_authors =round(prop_authors *100,2)) %>%
-arrange(desc(citations))
-
-gt_top5_communities_citations <- top5_communities_citations %>%
-gt() %>%
-tab_header(title ="Proportion of citations and authors for each community") %>%
-cols_label(community ="Community",
-citations ="Number of citations",
-n ="Number of authors",
-prop_citations ="Proportion of citations (in %)",
-prop_authors ="Proportion of authors (in %)",
-cumul_prop_citations ="Cumulative proportion of citations (in %)",
-cumul_prop_authors ="Cumulative proportion of authors (in %)") %>%
-cols_align(align ="center", columns =everything())
-
-# save gt in png
-#gtsave(gt_top5_communities_citations, "images/top5_communities_citations.png")
-#gtsave(gt_top5_communities_citations, filename = "top5_communities_citations.png")
-
-
-gt_top5_communities_citations
-
-
-
-
-
-
-
-
-
Proportion of citations and authors for each community
-
-
-
-
Community
-
Number of citations
-
Number of authors
-
Proportion of citations (in %)
-
Proportion of authors (in %)
-
Cumulative proportion of citations (in %)
-
Cumulative proportion of authors (in %)
-
-
-
-
21
-
6050
-
28
-
18.76
-
3.03
-
18.76
-
3.03
-
9
-
5224
-
35
-
16.20
-
3.79
-
34.96
-
6.83
-
45
-
1833
-
20
-
5.68
-
2.17
-
40.64
-
8.99
-
29
-
1321
-
23
-
4.10
-
2.49
-
44.74
-
11.48
-
57
-
403
-
20
-
1.25
-
2.17
-
45.99
-
13.65
-
-
-
-
-
-
-
-Code
-
#pareto.chart(citations_per_community$citations, ylab = "Cumulative proportion of citations", col = "steelblue", main = "Pareto chart of the citations per community")
-
-
-
Code
-
#model <- lm(citations ~ inverse_burt_weighted + closeness + community + color, data = df_0_2023)
-#summary(model)
-#plot(density(df_0_2023$citations), main = "Density Plot of Citations", xlab = "Citations")
-#hist(df_0_2023$citations, main = "Distribution of Citations", xlab = "Citations")
-
-#shapiro.test(residuals(model))
-
-#plot(model, which = 1)
-
-#stargazer::stargazer(model, type = "text")
-
-#library(MASS)
-
-#model_negbin <- glm.nb(citations ~ inverse_burt_weighted + closeness + community + color, data = df_0_2023)
-
-#summary(model_negbin)
-#stargazer::stargazer(model_negbin, type = "text")
+
#pareto.chart(citations_per_community$citations, ylab = "Cumulative proportion of citations", col = "steelblue", main = "Pareto chart of the citations per community")
@@ -3270,1383 +2752,1420 @@
Source Code
-
---
-title: "Systematic Literature Review"
-date: "today"
-date-format: long
-author:
- - name: Olivier Caron
- email: olivier.caron@dauphine.psl.eu
- affiliations:
- name: "Paris Dauphine - PSL"
- city: Paris
- state: France
- - name: Christophe Benavent
- email: christophe.benavent@dauphine.psl.eu
- affiliations:
- name: "Paris Dauphine - PSL"
- city: Paris
- state: France
-toc: true
-number-sections: true
-number-depth: 10
-format:
- html:
- theme:
- light: yeti
- #dark: darkly
- code-fold: true
- code-summary: "Display code"
- code-tools: true #enables to display/hide all blocks of code
- code-copy: true #enables to copy code
- grid:
- body-width: 1000px
- margin-width: 100px
- toc: true
- toc-location: left
-execute:
- echo: true
- warning: false
- message: false
-editor: visual
-fig-align: "center"
-highlight-style: ayu
-css: styles.css
-reference-location: margin
-bibliography: slrbib.bib
----
-
-# Introduction
-
-```{r}
-#| label : libraries-data-r
-knitr::opts_chunk$set(echo =TRUE,message=FALSE, warning=FALSE)
-library(ape)
-library(cowplot)
-library(DT)
-library(ggcorrplot)
-library(ggplot2)
-library(ggrepel)
-library(ggthemes)
-library(ggwordcloud)
-library(gt)
-library(heatmaply)
-library(plotly)
-library(quanteda)
-library(quanteda.textmodels)
-library(quanteda.textstats)
-library(RColorBrewer)
-library(reactable)
-library(reticulate)
-library(Rtsne)
-library(splitstackshape)
-library(tidyr)
-library(webshot)
-library(word2vec)
-
-theme_set(theme_minimal())
-
-library(tidyverse)
-df <-read_delim("nlp_full_data_final_18-08-2023.csv",delim =";",
-escape_double =FALSE, trim_ws =TRUE) %>%
-select(1,5,7,8, 14, 15,16,21,23,32,33,39,40,51,52,53) %>%
-group_by(entry_number) %>%
-filter(row_number()==1) %>%
-rename(id=1,title =8, review=9, text=10, keywords =13)
-
-df$review2<-ifelse(str_detect(df$review,"Proceedings")==TRUE, "out", df$review)
-df$review2<-ifelse(str_detect(df$review2, "Conference")==TRUE, "out", df$review2)
-df$review2<-ifelse(str_detect(df$review2, "Transactions")==TRUE, "out", df$review2)
-
-# for the networks
-list_articles <-read_csv2("nlp_full_data_final_18-08-2023.csv") %>%
-select(1,5,7,8, 14, 15,16,21,23,32,33,39,40,51,52,53) %>%
-rename(id=1,title =8, review=9, text=10, keywords =13)
-
-list_articles$review2<-ifelse(str_detect(list_articles$review,"Proceedings")==TRUE, "out", list_articles$review)
-list_articles$review2<-ifelse(str_detect(list_articles$review2, "Conference")==TRUE, "out", list_articles$review2)
-list_articles$review2<-ifelse(str_detect(list_articles$review2, "Transactions")==TRUE, "out", list_articles$review2)
-list_articles <- list_articles %>%filter(review2 !="out")
-
-```
-
-
-```{python}
-#| label : libraries-data-python
-
-import community as community_louvain
-import pandas as pd
-import numpy as np
-import networkx as nx
-import matplotlib.pyplot as plt
-import plotly.express as px
-import re
-
-from ipysigma import Sigma, SigmaGrid
-from pyvis.network import Network
-from itertools import combinations
-from datetime import datetime
-from functools import partial
-
-list_articles = r.list_articles
-```
-
-# Description
-
-```{r}
-#| label : description
-#| fig-height: 7
-#| fig-width: 17
-#| column: page
-
-
-df<- df%>%filter(review2 !="out")
-t0 <-as.data.frame(table(df$review2))%>%
-filter(Freq>3)
-
-g01 <-ggplot(t0, aes(x=reorder(Var1, Freq), y=Freq)) +
-geom_bar(stat="identity", fill="steelblue") +
-coord_flip() +
-theme_minimal() +
-labs(title="Number of Articles per Journal", y="Proportion", x="") +
-theme(
-axis.title.y =element_blank(),
-axis.text.y =element_text(size =10)
- )
-
-t1<-as.data.frame(table(df$year))
-
-# Graph 2: Number of publications per year
-g02 <-ggplot(t1, aes(x=Var1, y=Freq, group=1)) +
-geom_line(size=1.1, color="steelblue") +
-geom_point(size=2, color="steelblue") +
-geom_smooth(color="#7D7C7C", linewidth=0.5)+
-theme_minimal() +
-labs(title="Number of Publications per Year", y="", x="Year") +
-theme(
-axis.text.x =element_text(angle =45, hjust =1, size =10),
-axis.title.x =element_blank(),
-plot.title =element_text(hjust =0.5)
- )
-
-plot_grid(g01, g02, labels =c('A', 'B'), label_size =8, ncol=2, rel_widths =c(1,1))
-plotgrid <-plot_grid(g01, g02, labels =c('A', 'B'), label_size =8, ncol=2, rel_widths =c(1,1))
-
-ggsave(filename="./images/quant.jpeg", plotgrid, width =40, height =18, units ="cm")
-
-
-```
-
-# Keywords
-
-```{r}
-#| label : keywords
-#| fig-height: 5
-#| fig-width: 10
-#| column: page
-#| fig-align: center
-
-user2<- df %>%
-select(id, keywords)%>%
-mutate(keywords=tolower(keywords))%>%
-separate(keywords, sep="\\|", into=c("A1","A2","A3","A4","A5","A6","A7","A8","A9","A10", "A11", "A12", "A13", "A14", "A15", "A16"))%>%
-pivot_longer(-id, names_to="Rang", values_to ="keywords") %>%
-filter(!is.na(keywords))
-
-test_user2 <- df %>%
-select(id, keywords) %>%
-mutate(keywords =tolower(keywords)) %>%
-cSplit('keywords', sep ="|", direction ="wide") %>%
-pivot_longer(-id, names_to ="Rang", values_to ="keywords", values_drop_na =TRUE)
-
-
-user2$keywords=str_trim(user2$keywords,side ="both")
-
-user2$keywords[user2$keywords=="artificial intelligence (ai)"]<-"ai"
-user2$keywords[user2$keywords=="automated analysis of text"]<-"automated text analysis"
-user2$keywords[user2$keywords=="automated textual analysis"]<-"automated text analysis"
-user2$keywords[user2$keywords=="natural language processinf"]<-"nlp"
-user2$keywords[user2$keywords=="natural language processing (nlp)"]<-"nlp"
-user2$keywords[user2$keywords=="natural language processing (nlp)-based approach"]<-"nlp"
-user2$keywords[user2$keywords=="nlp tools"]<-"nlp"
-user2$keywords[user2$keywords=="natural language processing"]<-"nlp"
-user2$keywords[user2$keywords=="online review"]<-"online reviews"
-user2$keywords[user2$keywords=="online shopping review"]<-"online reviews"
-user2$keywords[user2$keywords=="review"]<-"online reviews"
-user2$keywords[user2$keywords=="reviews"]<-"online reviews"
-user2$keywords[user2$keywords=="topic modelling"]<-"topic modeling"
-user2$keywords[user2$keywords=="user generated content"]<-"user-generated content"
-
-
-# compter les mots cles
-foo<- user2 %>%
-mutate(n=1) %>%
-group_by(id,keywords)%>%
-summarise(n=sum(n))
-
-foo1<- foo %>%
-group_by(keywords)%>%
-summarise(m=n())%>%
-filter(m>1)
-
-set.seed(42)
-ggplot(foo1, aes(label = keywords, size = m)) +
-geom_text_wordcloud() +
-scale_size_area(max_size =10) +
-theme_minimal()
-
-ggsave(filename="./images/keywords01.jpeg", plot=last_plot(), width =27, height =18, units ="cm")
-
-#projection tsne
-
-foo2<-user2 %>%
-select(-Rang)%>%
-group_by(id,keywords)%>%
-summarize(n=n()) %>%
-pivot_wider(id,names_from ="keywords", values_from ="n" )
-
-foo2<- foo2 %>%
-replace(is.na(.),0) %>%
- dplyr::select(-id)
-
-
-foo3<-t(foo2)
-tsne_out <-Rtsne(foo3,perplexity =10, dim=2, check_duplicates =FALSE) # Run TSNE
-tsne_out2<-as.data.frame(tsne_out$Y)
-keywords<-rownames(foo3)
-library(ggrepel)
-tsne_out3<-cbind(tsne_out2, keywords)
-tsne_out3<-merge(tsne_out3,foo1)
-tsneplot <- tsne_out3%>%
-filter(m>0) %>%
-ggplot(aes(x=V1, y=V2, label=keywords))+
-geom_point(aes(size=m), alpha=.5)+
-geom_text_repel(aes(label=ifelse(m>1,keywords,""),size=log(m)), max.overlaps =50)
-
-tsneplot
-ggsave(filename="./images/keywords.jpeg", tsneplot, width =27, height =18, units ="cm")
-```
-
-## With a breakdown of the keywords
-
-The strategy is to reduce the morphologies of the most frequent keywords, in the form of unigrams, and then re-decompose the terms that make up the non-recoded keywords.
-
-```{r}
-#| label : keywords2
-#| fig-height: 5
-#| fig-width: 10
-#| column: page
-#| fig-align: center
-
-key1<- df %>%
-select(id, keywords)%>%
-mutate(keywords=tolower(keywords))%>%
-separate(keywords, sep="\\|", into=c("A1","A2","A3","A4","A5","A6","A7","A8","A9","A10", "A11", "A12", "A13", "A14", "A15", "A16"))%>%
-pivot_longer(-id, names_to="Rang", values_to ="keywords") %>%
-filter(!is.na(keywords)) %>%
-mutate(keywords=str_squish(keywords),
-keywords=str_replace(keywords, "text mining", "textmining"),
-keywords=str_replace(keywords, "text-mining", "textmining"),
-keywords=str_replace(keywords, "text analysis", "textanalysis"),
-keywords=str_replace(keywords, "text-analysis", "textanalysis"),
-keywords=str_replace(keywords, "user-generated content", "ugc"),
-keywords=str_replace(keywords, "user-generated content-ugc", "ugc"),
-keywords=str_replace(keywords, "user generated content", "ugc"),
-keywords=str_replace(keywords, "user genrated content (ugc)", "ugc"),
-keywords=str_replace(keywords, "artificial intelligence", "ai"),
-keywords=str_replace(keywords, "artificial intelligence", "ai"),
-keywords=str_replace(keywords, "artificial intelligence (ai)", "ai"),
-keywords=str_replace(keywords, "artificial neural network", "ai"),
-keywords=str_replace(keywords, "artificial neural networks", "neural network"),
-keywords=str_replace(keywords, "natural language processing", "nlp"),
-keywords=str_replace(keywords, "natural language processing (nlp)", "nlp"),
-keywords=str_replace(keywords, "natural language processing (nlp)-based approach", "nlp"),
-keywords=str_replace(keywords, "natural language processing (nlp)-based approach", "nlp"),
-keywords=str_replace(keywords, "topic model analysis", "topics"),
-keywords=str_replace(keywords, "topic modeling", "topics"),
-keywords=str_replace(keywords, "topic modelling", "topics"),
-keywords=str_replace(keywords, "topic model", "topics"),
-keywords=str_replace(keywords, "e wom", "ewom"),
-keywords=str_replace(keywords, "e-wom", "ewom"),
-keywords=str_replace(keywords, "electronic word-of-mouth", "ewom"),
-keywords=str_replace(keywords, "electronic word of mouth", "ewom"),
-keywords=str_replace(keywords, "electronic word of mouth (ewom)", "ewom"),
-keywords=str_replace(keywords, "online word-of-mouth", "ewom"),
-keywords=str_replace(keywords, "online word of mouth", "ewom"),
-keywords=str_replace(keywords, "online word of mouth", "ewom"),
-keywords=str_replace(keywords, "negative word-of-mouth nwom", "ewom"),
-keywords=str_replace(keywords, "word-of-mouth communication", "ewom"),
-keywords=str_replace(keywords, "word-of-mouth", "ewom"),
-keywords=str_replace(keywords, "online review", "reviews"),
-keywords=str_replace(keywords, "online reviews", "reviews"),
-keywords=str_replace(keywords, "social media", "socialmedia"),
-keywords=str_replace(keywords, "latent dirichlet allocation", "lda"),
-keywords=str_replace(keywords, "latent dirichlet allocation (lda)", "lda"),
-keywords=str_replace(keywords, "latent dirichlet allocation algorithm", "lda"),
-keywords=str_replace(keywords, "word embedding", "embedding"),
-keywords=str_replace(keywords, "word2vec", "embedding"),
-keywords=str_replace(keywords, "latent dirichlet allocation model", "lda"),
-keywords=str_replace(keywords, "machine learning", "ml"),
-keywords=str_replace(keywords, "machine learning and linguistic analysis", "ml"),
-keywords=str_replace(keywords, "big data", "bigdata"),
-keywords=str_replace(keywords, "liwc", "liwc"),
-keywords=str_replace(keywords, "linguistic inquiry and word count liwc", "liwc"),
-keywords=str_replace(keywords, "bibliometrics", "bibliometric"),
-keywords=str_replace(keywords, "\\(", ""),
-keywords=str_replace(keywords, "\\)", "")
- )
-foo<-key1%>%group_by(keywords)%>%
-summarize(n=n())
-foo
-
-key2<- key1 %>%
-select(-Rang) %>%
-separate(keywords, sep=" ", into=c("A1","A2","A3","A4","A5","A6","A7","A8","A9","A10", "A11", "A12", "A13", "A14", "A15", "A16")) %>%
-pivot_longer(-id, names_to="Rang", values_to ="keywords") %>%
-mutate(n=nchar(keywords)) %>%
-filter(!is.na(keywords))%>%
-filter(n>0) %>%select(-n)
-
-key3<-key2%>%
-group_by(keywords)%>%
-summarize(k=n())
-
-foo2<-key2 %>%
-group_by(id,keywords)%>%
-summarize(m=n()) %>%
-pivot_wider(id,names_from ="keywords", values_from ="m", names_repair="unique" )
-
-foo2<- foo2 %>%
-replace(is.na(.),0) %>%
- dplyr::select(-1)
-
-
-foo3<-t(foo2)
-tsne_out <-Rtsne(foo3,perplexity =25, dim=2, check_duplicates =FALSE) # Run TSNE
-tsne_out2<-as.data.frame(tsne_out$Y)
-keywords<-rownames(foo3)
-library(ggrepel)
-tsne_out3<-cbind(tsne_out2, keywords)
-tsne_out3<-merge(tsne_out3,key3)
-
-
-tsne_out3%>%
-ggplot(aes(x=V1, y=V2, label=keywords))+
-geom_text_repel(aes(label=ifelse(k>4,keywords,""),size=k), max.overlaps =50)
-
-
-ggsave(filename="./images/keywords03.jpeg", plot=last_plot(), width =27, height =18, units ="cm")
-
-
-foo<- df%>%select(id,year)
-
-foo5<-key2 %>%
-group_by(id,keywords)%>%
-summarize(m=n()) %>%
-left_join(foo) %>%
-group_by(year,keywords)%>%
-summarise(m=sum(m))%>%
-left_join(key3)%>%
-filter(keywords=="ai"| keywords=="textmining"| keywords=="nlp"| keywords=="topics"|
- keywords=="lda"| keywords=="ml"|keywords=="embbeding"|keywords=="liwc")
-
-ggplot(foo5, aes(x=year, y=m, group=keywords))+geom_area(aes(fill=keywords))
-
-foo5<-key2 %>%
-group_by(id,keywords)%>%
-summarize(m=n()) %>%
-left_join(foo) %>%
-group_by(year,keywords)%>%
-summarise(m=sum(m))%>%
-left_join(key3)%>%
-filter(keywords=="ewom"| keywords=="reviews"| keywords=="socialmedia"| keywords=="ugc"| keywords=="bibliometric")
-
-ggplot(foo5, aes(x=year, y=m, group=keywords))+geom_area(aes(fill=keywords))
-
-```
-
-# Structural topic model (STM)
-
-## Data preparation
-
-```{r}
-#| label: data-preparation
-
-library(quanteda)
-library(stm)
-df$Texte<-paste(df$title, " . ", df$text, df$keywords)
-
-df$year<-ifelse(df$year<2011,2010,
-ifelse(df$year>2010& df$year<2016,2015,
-ifelse(df$year>2015& df$year<2020,2019,df$year)))%>%
-as.character()
-table(df$year)
-corpus <- quanteda::corpus(
- df,
-text_field ="Texte",
-docid_field ="id",
-unique_docnames =FALSE
- )
-
-corpus_dfm <-
- quanteda::dfm(corpus) %>%
- quanteda::dfm_trim(
-# tokens_remove = 'nan',
-# Get rid of any word that doesn't occur at least x
-min_termfreq =5,
-# Get rid of any word that is in at least x per cent of documents
-max_docfreq =0.9,
-# Below we specify percentages - we can specify counts or ranks
-docfreq_type ="prop"
- ) %>%
- quanteda::dfm_keep(
-# '[a-zA-Z]', # filter out terms without alpha
-# valuetype='regex',
-min_nchar =2) # Only words with at least 3 characters
-
-out <- quanteda::convert(corpus_dfm, to ='stm')
-
-
-K =16# number of topics
-
-FORMULA_RANGE <-paste("1",as.character(K), sep=":")
-PREVALENCE_FORMULA <-as.formula(paste("~", "year"))
-PREVALENCE_FORMULA_EFFECT <-as.formula( paste(FORMULA_RANGE, paste("~ ", "year", collapse="+") ) )
-
-OUTPUT_PATH <-"./"
-OUTPUT_FILE <-sprintf("STM_model_%s.RData", K)
-
-message(PREVALENCE_FORMULA)
-message(PREVALENCE_FORMULA_EFFECT)
-message(OUTPUT_FILE)
-
-```
-
-## Estimation
-
-```{r}
-#| label: estimation
-
-t1=Sys.time()
-stm_model <- stm::stm(
-documents = out$documents,
-vocab = out$vocab,
-data = out$meta,
-K = K,
-prevalence = PREVALENCE_FORMULA,
-verbose =TRUE, # show progress
-init.type ="Spectral",
-seed =56,
-emtol =1e-05,
-max.em.its=40
- )
-t2=Sys.time()
-
-
-stm_effects <- stm::estimateEffect(PREVALENCE_FORMULA_EFFECT,
-stmobj = stm_model, metadata = out$meta)
-
-saveRDS(stm_model, "stm_model.rds")
-saveRDS(stm_effects, "stm_effects.rds")
-
-
-stm_model<-readRDS("stm_model.rds")
-stm_effects<-readRDS("stm_effects.rds")
-
-labelTopics(stm_model, topics =NULL, n =5, frexweight =0.5)
-
-```
-
-## Topics' description (beta)
-
-4 indicators: probability, exclusivity, resulting frex, lift and score.
-
-$FREX = \left(\frac{w}{F} + \frac{1-w}{E}\right)^{-1}$
-
-see lift: $Lift = \beta_{k,v}/(w_v/\sum_v w_v)$
-
-the score: $\beta_{v, k} (\log \beta_{w,k} - 1 / K \sum_{k'} \log \beta_{v,k'})$
-
-```{r}
-#| label: topics-description
-#| fig-height: 7
-#| fig-width: 12
-#| column: screen-right
-#| fig-align: center
-beta<-t(stm_model$beta$logbeta[[1]]) %>%
-as.data.frame()
-
-logbeta<-stm_model$beta$logbeta[[1]]
-
-vocab<-stm_model$vocab %>%
-as.data.frame()
-
-vocab<- vocab %>% dplyr::rename(feature=1)
-
-#frequence
-
-beta2<-cbind(vocab, beta) %>%
-pivot_longer(-feature, names_to="topic", values_to ="logprob") %>%
- dplyr::group_by(topic)%>%
- dplyr::mutate(r=rank(desc(logprob)),
-p=exp(logprob)) %>%
- dplyr::filter(r<31)
-
-#labelisation des topics
-
-beta3<-calcfrex(logbeta, w =0.25, wordcounts =NULL)
-
-beta4<-cbind(vocab, beta3) %>%
-as.data.frame() %>%
-pivot_longer(-feature, names_to="topic2", values_to ="frex") %>%
-mutate(topic=paste0("V",topic2))
-
-
-beta2<- beta2 %>%
-left_join(beta4, by=c("feature", "topic"))
-
-synth<-labelTopics(stm_model, topics =NULL, n =5, frexweight =0.25)
-prob<-synth$prob
-frex<-synth$frex
-
-#intitulés suggérés par Bard
-
-beta2$topic[beta2$topic=="V1"]<-"T01 Analyse bibliométrique\ndes recherches marketing"
-beta2$topic[beta2$topic=="V2"]<-"T02 Qualité du service en ligne"
-beta2$topic[beta2$topic=="V3"]<-"T03 Publicité en ligne et contenu personnalisé"
-beta2$topic[beta2$topic=="V4"]<-"T04 Personnalisation du produit et régulation"
-beta2$topic[beta2$topic=="V5"]<-"T05 Consumer exp dictionnary "#il a du mal
-beta2$topic[beta2$topic=="V6"]<-"T06 Avis en ligne et sentiment "
-beta2$topic[beta2$topic=="V7"]<-"T07 Customer online review and satisfaction"
-beta2$topic[beta2$topic=="V8"]<-"T08 Tourism"
-beta2$topic[beta2$topic=="V9"]<-"T09 Decoding Guest Satisfaction Through Airbnb Reviews"
-beta2$topic[beta2$topic=="V10"]<-"T10 Social Media Analysis"
-beta2$topic[beta2$topic=="V11"]<-"T11 Specific Macau"
-beta2$topic[beta2$topic=="V12"]<-"T12 Online Brand Strategy"
-beta2$topic[beta2$topic=="V13"]<-"T13 Text Data for Marketing Decisions"
-beta2$topic[beta2$topic=="V14"]<-"T14 Social Media Engagement"
-beta2$topic[beta2$topic=="V15"]<-"T15 Text Analytics in Market Segmentation"
-beta2$topic[beta2$topic=="V16"]<-"T16 Ewom"
-
-
-#la transparence rend compte de l'exclusitivité frex
-stm_topics <-ggplot(beta2)+
-geom_text_wordcloud(aes(label=feature, size=p, alpha=2100-frex), color="darkblue")+
-facet_wrap(vars(topic), ncol=4)+
-scale_size_area(max_size =7)+
-theme(strip.text =element_text(size =10))
-
-ggsave(paste0("./images/stm_topic",K,".jpg"),
- stm_topics,
-width =28,
-height =20,
-units ="cm",
-dpi =300)
-
-
-```
-
-## Prevalence description (beta)
-
-```{r}
-#| label: prevalence-description
-#| fig-height: 5
-#| fig-width: 10
-#| fig-align: center
-foo<-stm_effects$parameters
-
-library(tidyr)
-library(plyr)
-
-#pour separer les liste et les transformer en df
-#attention ça pertube
-foo1<-ldply(foo, data.frame)%>%
-select(contains('est'))
-
-library(dplyr)
-
-
-F<-data.frame(param=c("cte", "2015", "2019","2020","2021","2022","2023"))
-
-
-# autant de fois que K
-param<-rbind(F, F, F, F, F, F, F, F, F, F,
- F, F, F, F, F ,F)
-
-
-R<-data.frame(topic=c(
-"T01 bibliometrics consumer",
-"T02 Service quality review",
-"T03 Ads and privacy",
-"T04 Customization",
-"T05 Consumer exp dictionnary ",
-"T06 Product review sentiment ",
-"T07 customer online review",
-"T08 Tourism",
-"T09 Airbnb",
-"T10 Social media",
-"T11 specific",
-"T12 Branding",
-"T13 marketing analytics",
-"T14 engagement media sociaux",
-"T15 marketing strategy",
-"T16 e wom"
-))
-
-#autant de fois que de paramètres
-R<-rbind(R, R, R, R, R,
- R, R ) %>%arrange(topic)
-
-
-
-foo2<-cbind(foo1, R, param)%>%
-pivot_longer(-c("topic","param"), names_to ="sim", values_to ="est") %>%
- dplyr::group_by(topic,param)%>%
- dplyr::summarise(mean=mean(est, na.rm=TRUE),
-se =sd(est)/sqrt(24),
-t=abs(mean)/se) %>%
- dplyr::mutate(param=ifelse(param=="cte", "2010", param))
-
-foo3<-cbind(foo1, R, param)%>%
-pivot_longer(-c("topic","param"), names_to ="sim", values_to ="est") %>%
- dplyr::group_by(topic,param)%>%
- dplyr::summarise(mean=mean(est, na.rm=TRUE),
-se =sd(est)/sqrt(24),
-t=abs(mean)/se) %>%
- dplyr::filter(param=="cte")%>%
- dplyr::select(-2,-4,-5) %>%
- dplyr::rename(cte = mean)
-
-foo4<-foo2 %>%left_join(foo3) %>%
-mutate(mean2=ifelse(param!="2010",mean+cte,mean))
-
-
-# dplyr::mutate(time=ifelse(str_sub(param, 1,1)=="0" |str_sub(param, 1,1)=="1" , "no", "yes"))
-
-
-
-
-
-stm_effect <-ggplot(foo4, aes(x=param,y=mean2,group=topic))+
-geom_bar(stat="identity")+
-# geom_smooth(se = FALSE )+
-facet_wrap(vars(topic), scale="fixed",ncol =4)+
-theme(axis.text.y =element_text(size=5))+
-theme(axis.text.x =element_text(size=5))+
-labs(x=NULL, y="prévalence")
-
-stm_effect
-
-
-ggsave(paste0("./images/stm_effect",K,".jpg"),
- stm_effect,
-width =28,
-height =20,
-units ="cm",
-dpi =300)
-
-
-```
-
-## The law of entropy
-
-```{r}
-#| label: entropy
-#| fig-height: 5
-#| fig-width: 8
-#| column: page
-#| fig-align: center
-nb.cols <-16
-mycolors <-colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
-
-
-ggplot(foo4, aes(x=param,y=mean2,group=topic))+
-geom_bar(stat="identity", aes(fill=topic))+
-# geom_smooth(se = FALSE )+
-theme(axis.text.y =element_text(size=5))+
-theme(axis.text.x =element_text(size=5))+
-labs(x=NULL, y="prévalence")+
-scale_fill_manual(values = mycolors)
-
-
-#entropie
-
-foo5<-foo4%>%select(1,2,7)%>%
-mutate(e=mean2*log(mean2)) %>%
-group_by(param) %>%
- dplyr::summarise(entropie=-sum(e))
-
-foo5$param<-as.numeric(foo5$param)
-ggplot(foo5,aes(x=param, y=entropie, group =1))+
-geom_point(stat ="identity")+geom_smooth(se=FALSE)+ylim(1.5,3)
-
-```
-
-# Networks of authors
-
-## Check name of authors
-
-We need to check if there are more than one unique `authorname` per `authid`. If so, we need to change the different names of author to the same name in order to have the exact same node per author later in the network.
-
-```{r}
-#| label: check-name-authors
-
-test <- list_articles %>%
-group_by(authid) %>%
-select(authid, authname, id) %>%
- dplyr::mutate(n =n()) %>%
- ungroup
-
-result <- test %>%
-group_by(authid) %>%
-filter(n_distinct(authname) >1) %>%
-distinct(authid, .keep_all =TRUE)
-
-result %>%reactable()
-
-number_duplicates <-nrow(result)
-
-cat("There are ", number_duplicates, " authors registered with different names.")
-```
-
-## Correct the duplicate names
-
-Let's correct that by using one property of the distinct function: the `.keep_all = TRUE` parameter. It keeps the first occurrence of each group, which is the first row encountered for each unique combination of `authid` and `authname`. It will be faster than manually changing the name of each author.
-
-```{r}
-#| label: change-name-authors
-
-# Merge list_articles with result on the authid column
-merged_df <-left_join(list_articles, result, by ="authid")
-
-# Replace authname values in list_articles with those from result
-list_articles$authname <-ifelse(!is.na(merged_df$authname.y), merged_df$authname.y, list_articles$authname)
-
-cat("There are", n_distinct(list_articles$id), "articles and", n_distinct(list_articles$authname), "authors overall in the data.")
-
-# Write the updated dataframe to a CSV file
-write_csv2(list_articles, "nlp_full_data_final_unique_author_names.csv")
-```
-
-It is now done. We can check again if there are more than one unique `authorname` per `authid`.
-
-## Verification of duplicate names
-
-```{r}
-#| label: check-unique-name-authors
-
-test <- list_articles %>%
-group_by(authid) %>%
-select(authid, authname, id) %>%
- dplyr::mutate(n =n())
-
-result <- test %>%
-group_by(authid) %>%
-filter(n_distinct(authname) >1) %>%
-distinct(authid, .keep_all =TRUE) %>%
-relocate(id)
-
-result %>%reactable()
-
-```
-
-## Construct the networks
-
-```{python}
-#| label: function-network
-
-# Constants to include in the network as hovering text for nodes
-COLUMNS_TO_COLLECT = [
-'affilname', 'affiliation_country', 'title', 'review', 'year', 'citedby_count', 'subjects_area', 'keywords'
-]
-
-def get_author_info(filtered_articles, columns):
-"""
- Given a DataFrame of filtered articles and a list of column names,
- this function collects author information and returns it as a dictionary.
- """
- author_info = {col: {} for col in columns}
- author_info["citations"] = {}
-
-for _, row in filtered_articles.iterrows():
- author_name = row['authname']
-
-if pd.notna(row['citedby_count']):
- author_info["citations"][author_name] = author_info["citations"].get(author_name, 0) + row['citedby_count']
-
-for col in columns:
- value = row[col]
-if pd.notna(value):
- value =str(value).strip()
-if author_name in author_info[col]:
-if value notin author_info[col][author_name]:
- author_info[col][author_name] +=" | "+ value
-else:
- author_info[col][author_name] = value
-
-return author_info
-
-def sigma_graph(dataframe, start_year, end_year):
-"""
- Creates a graph representing author collaborations based on a given DataFrame of articles.
- Filters the articles based on the given start and end years.
- """
-# Error handling
-if dataframe.empty:
-print("The DataFrame is empty.")
-returnNone, None
-
-for column in COLUMNS_TO_COLLECT:
-if column notin dataframe.columns:
-print(f"The DataFrame is missing the column: {column}")
-returnNone, None
-
- list_articles = dataframe
- filtered_articles = list_articles[(list_articles['year'] >= start_year) & (list_articles['year'] <= end_year)]
-
- author_pairs = []
- grouped = filtered_articles.groupby('id')[['authid', 'authname']].agg(list).reset_index()
-
-for _, row in grouped.iterrows():
-id= row['id']
- authors = row['authid']
- authnames = row['authname']
-
-iflen(authors) ==1:
- author_pairs.append((id, authors[0], authors[0], authnames[0], authnames[0]))
-eliflen(authors) >1:
- author_combinations =list(combinations(range(len(authors)), 2))
-for i, j in author_combinations:
- author_pairs.append((id, authors[i], authors[j], authnames[i], authnames[j]))
-
- result_df = pd.DataFrame(author_pairs, columns=['id', 'authid1', 'authid2', 'authname1', 'authname2'])
-
- collaboration_df = result_df[["authname1", "authname2"]]
- collaboration_df = pd.DataFrame(np.sort(collaboration_df.values, axis=1), columns=collaboration_df.columns)
- collaboration_df['value'] =1
- collaboration_df = collaboration_df.groupby(["authname1", "authname2"], sort=False, as_index=False).sum()
-
- G = nx.from_pandas_edgelist(collaboration_df, 'authname1', 'authname2', edge_attr='value', create_using=nx.Graph())
-
-for u, v in G.edges:
- G[u][v]["color"] ="#7D7C7C"
-
-for index, row in collaboration_df.iterrows():
- G.add_edge(row['authname1'], row['authname2'], weight=row['value'])
-
- metrics = {
-'centrality': nx.degree_centrality,
-'betweenness': nx.betweenness_centrality,
-'closeness': nx.closeness_centrality,
-'eigenvector_centrality': partial(nx.eigenvector_centrality, max_iter=1000),
-'burt_constraint_weighted': partial(nx.constraint, weight="value"),
-'burt_constraint_unweighted': nx.constraint
- }
-
-for attr, func in metrics.items():
- nx.set_node_attributes(G, func(G), attr)
-
- author_info = get_author_info(filtered_articles, COLUMNS_TO_COLLECT)
-
-for col in COLUMNS_TO_COLLECT:
- nx.set_node_attributes(G, author_info[col], col)
-
- nx.set_node_attributes(G, author_info['citations'], 'citations')
-
-# Compute the inverse burt constraint to plot the lowest values as the biggest nodes
-# (= authors that are the less constrained in their network => multiple probable collaborations)
-for node in G.nodes:
-# Check if the 'burt_constraint_weighted' metric exists for the node
-if'burt_constraint_weighted'in G.nodes[node]:
- burt_score = G.nodes[node]['burt_constraint_weighted']
-# Calculate the inverse, avoiding division by zero
- G.nodes[node]['inverse_burt_weighted'] =1/ burt_score if burt_score !=0else0
-else:
-# Handle the case where the 'burt_constraint_weighted' metric is not available for this node
-# For example, by setting the value to None or a default value
- G.nodes[node]['inverse_burt_weighted'] =None# or another default value
-
-# Compute Louvain commmunities
- partition = community_louvain.best_partition(G)
-
-for node, comm_number in partition.items():
- G.nodes[node]['community'] = comm_number
-
-# Color the graph using the greedy coloring algorithm with the 'largest_first' strategy
- colors = nx.greedy_color(G, strategy='largest_first', interchange=False)
-
-# Set the computed colors as an attribute to each node in the graph
- nx.set_node_attributes(G, colors, 'color')
-# Now, each node in the graph G has an attribute 'color' that corresponds to the color assigned by the greedy_color function
-
-# Computer HITS scores
- hubs, authorities = nx.hits(G, max_iter=100, nstart=None, normalized=False)
-
-# Set the computed scores as an attribute to each node in the graph
- nx.set_node_attributes(G, hubs, 'hub_score')
- nx.set_node_attributes(G, authorities, 'authority_score')
-
-
-# Creating a list of dictionaries containing the attributes of each node
- data_for_df = []
-for node in G.nodes(data=True):
-# `node` is a tuple (node_name, attributes_dict)
- node_data = node[1] # Extracting the attributes dictionary
- node_data['Node'] = node[0] # Adding the node name as an attribute
-
-# Adding the attributes dictionary of this node to the list
- data_for_df.append(node_data)
-
-# Creating a DataFrame from the list of dictionaries
- df_nodes = pd.DataFrame(data_for_df)
-
- Sigma.write_html(G,
- default_edge_type ="curve", # Default edge type
- clickable_edges =True, # Clickable edges
- edge_size ="value", # Set edge size
- fullscreen =True, # Display in fullscreen
- label_density =3, # Label density (= increase to have more labels appear at normal zoom level)
- label_font ="Helvetica Neue", # Label font
- max_categorical_colors =10, # Max categorical colors
- node_border_color_from ='node', # Node border color from node attribute
- node_color ="community", # Set node colors
- node_label_size ="citations", # Node label size
-#node_label_size_range = (12, 36), # Node label size range
- node_label_size_range = (12, 30), # Node label size range
-#node_metrics = {"community": {"name": "louvain", "resolution": 1}}, # Specify node metrics
- node_size ="citations", # Node size
-#node_size_range = (3, 30), # Node size range
- node_size_range = (2, 20), # Node size range
- path =f"networks/authors/{start_year}_{end_year}_VF.html", # Output file path
- start_layout =3, # Start layout algorithm
-#node_border_color = "black", # Node border color
-#edge_color = "#7D7C7C" # Edge color
-# node_label_color = "community" # Node label color
- )
-
-
-return G, df_nodes
-```
-
-## Create the overall network (from 1996 to 2023)
-
-```{python}
-#| label: create-network-overall
-
-print("The year range of the publications is from {} to {}".format(int(list_articles['year'].min()), int(list_articles['year'].max())))
-
-#Let's try each year from 2015 as end year onwards
-
-
-G_1996_2015, df_1996_2015 = sigma_graph(list_articles, 1996, 2015)
-G_1996_2016, df_1996_2016 = sigma_graph(list_articles, 1996, 2016)
-G_1996_2017, df_1996_2017 = sigma_graph(list_articles, 1996, 2017)
-G_1996_2018, df_1996_2018 = sigma_graph(list_articles, 1996, 2018)
-G_1996_2019, df_1996_2019 = sigma_graph(list_articles, 1996, 2019)
-G_1996_2020, df_1996_2020 = sigma_graph(list_articles, 1996, 2020)
-G_1996_2021, df_1996_2021 = sigma_graph(list_articles, 1996, 2021)
-G_1996_2022, df_1996_2022 = sigma_graph(list_articles, 1996, 2022)
-G_1996_2023, df_1996_2023 = sigma_graph(list_articles, 1996, 2023)
-
-networks = {
-'1996-2015': G_1996_2015,
-'1996-2016': G_1996_2016,
-'1996-2017': G_1996_2017,
-'1996-2018': G_1996_2018,
-'1996-2019': G_1996_2019,
-'1996-2020': G_1996_2020,
-'1996-2021': G_1996_2021,
-'1996-2022': G_1996_2022,
-'1996-2023': G_1996_2023
-}
-
-
-max(nx.find_cliques(G_1996_2015), key=len)
-max(nx.find_cliques(G_1996_2016), key=len)
-max(nx.find_cliques(G_1996_2017), key=len)
-max(nx.find_cliques(G_1996_2018), key=len)
-max(nx.find_cliques(G_1996_2019), key=len)
-max(nx.find_cliques(G_1996_2020), key=len)
-max(nx.find_cliques(G_1996_2021), key=len)
-max(nx.find_cliques(G_1996_2022), key=len)
-max(nx.find_cliques(G_1996_2023), key=len)
-
-
-```
-
-```{=html}
-<iframe width="1500" height="900" src="networks/authors/1996_2023_sigma_burt_SLR.html" title="Sigma graph" frameborder=0 class="column-page"></iframe>
-```
-# Analysis of the networks
-
-Let's sort by the Burt constraint weighted and see the top 10 authors with the lowest Burt constraint weighted.
-You can filter by different metrics: Burt's constraint, number of citations, betweenness centrality, closeness centrality, hub score, authority score (HITS)...
-
-```{r}
-#| label: analysis-network
-#| column: page
-
-df_1996_2023 <- py$df_1996_2023
-
-reactable_burt <- df_1996_2023 %>%
- dplyr::select(Node, citations, burt_constraint_weighted, betweenness, closeness, hub_score, authority_score, affilname, affiliation_country, community) %>%
-arrange(burt_constraint_weighted)
-
-reactable(reactable_burt, searchable =TRUE, defaultPageSize =10, highlight =TRUE, bordered =TRUE, striped =TRUE, compact =TRUE, defaultColDef =colDef(
-headerStyle =list(background ="#e6e6e6", fontWeight ="bold", fontSize ="12px"),
-style =list(fontSize ="12px")
-))
-
-```
-
-
-```{r}
-#| label: correlation-matrix-2
-
-# Select only the numeric columns from the dataframe
-df_numeric <- df_1996_2023[sapply(df_1996_2023, is.numeric)]
-
-# Calculate the correlation matrix and round the values to one decimal point
-corr <-round(cor(df_numeric, use ="complete.obs"), 2)
-
-# Calculate a matrix of p-values for the correlations
-# Note: 'cor_pmat' is typically a custom function or part of a specific package.
-# Make sure you have defined or installed any package that includes this function.
-p.mat <-cor_pmat(df_numeric)
-
-# Visualize the lower triangle of the correlation matrix using ggcorrplot
-# The heatmap color indicates the correlation coefficient between variables
-# 'hc.order' is set to TRUE to reorder the matrix using hierarchical clustering
-# 'type' is set to "lower" to show only the lower triangle of the matrix
-# 'outline.col' is set to "white" to define the border color around the squares
-corr.plot <-ggcorrplot(
- corr, hc.order =TRUE, type ="lower", outline.col ="white",
-p.mat = p.mat
-)
-
-# Convert the ggcorrplot object to an interactive plotly object
-# This allows for interactive features such as tooltips on hover
-ggplotly(corr.plot)
-```
-
-```{r}
-#| label: correlation-matrix-3
-
-# Compute correlation coefficients
-cor.coef <-cor(df_numeric)
-
-# Compute correlation p-values
-cor.test.p <-function(x){
- FUN <-function(x, y) cor.test(x, y)[["p.value"]]
- z <-outer(
-colnames(x),
-colnames(x),
-Vectorize(function(i,j) FUN(x[,i], x[,j]))
- )
-dimnames(z) <-list(colnames(x), colnames(x))
- z
-}
-p <-cor.test.p(df_numeric)
-
-
-# Create the heatmap
-heatmaply_cor(
- cor.coef,
-node_type ="scatter",
-point_size_mat =-log10(p),
-point_size_name ="-log10(p-value)",
-label_names =c("x", "y", "Correlation")
-)
-```
-
-## An interesting metric: the graph density
-
-The graph density is the ratio of the number of edges to the maximum number of possible edges. It is a measure of the proportion of edges present in a graph. A graph with a high density has a large number of edges compared to the number of nodes. A graph with a low density has a small number of edges compared to the number of nodes.
-
-A more formal definition is given [here](https://networkx.org/documentation/stable/reference/generated/networkx.classes.function.density.html) by the following formulas:
-
-- For undirected graphs:
-
-$$
-\begin{equation}d=\frac{2 m}{n(n-1)}\end{equation}
-$$
-- For directed graphs:
-
-$$
-\begin{equation}d=\frac{m}{n(n-1)}\end{equation}
-$$
-
-where $n$ is the number of nodes and $m$ is the number of edges in the graph.
-
-From an interpretation standpoint, we can appreciate the density in the graphs bellow as follows:
-
-+--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| $d$ | **Interpretation** |
-+:============:+:===========================================================================================================================================================+
-| Close to $0$ | - The collaborative relationships among authors are sparse: |
-| | |
-| | - Authors have limited connections with each other outside of their community. |
-| | |
-| | - Scientific papers are primarily the work of individual authors or small isolated groups. |
-+--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| Close to $1$ | - Authors frequently collaborate with one another, leading to a web of interconnected scientific collaborations. |
-| | |
-| | - Scientific papers often involve contributions from multiple authors, reflecting a high level of teamwork and interdisciplinary research. |
-| | |
-| | - Collaborations are a significant aspect of the research process in this marketing field, and authors actively seek out opportunities to work together. |
-| | |
-| | - The network of collaborations is well-established and robust, facilitating the exchange of ideas and the advancement of scientific knowledge. |
-+--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
-
-Let's compare the density of multiple networks over time :
-
-```{python}
-#| label: density-networks
-
-# Compute Density of all the networks created above with networkx
-
-density_df = pd.DataFrame(columns=["network", "density"])
-
-temp_dfs = []
-
-for network_name, network in networks.items():
- density = nx.density(network)
- nb_nodes = network.number_of_nodes()
- nb_edges = network.number_of_edges()
- linear_density = nb_nodes / nb_edges if nb_edges !=0else0
- nb_communities =len(set(nx.get_node_attributes(network, "community").values()))
- nb_mean_nodes_per_community = nb_nodes / nb_communities
- temp_df = pd.DataFrame([{
-"network": network_name,
-"density": density,
-"nb_edges": nb_edges,
-"nb_nodes": nb_nodes,
-"linear_density": linear_density,
-"nb_communities": nb_communities,
-"nb_mean_nodes_per_community": nb_mean_nodes_per_community
- }])
- temp_dfs.append(temp_df)
-
-# Concat all the temp_dfs into one dataframe
-density_df = pd.concat(temp_dfs, ignore_index=True)
-```
-
-```{r}
-#| label: density-networks-2
-#| column: screen-inset-right
-#| fig.width: 15
-#| fig.height: 20
-#| fig-dpi: 300
-#| fig.align: center
-
-density_df <- py$density_df
-
-density_long <- density_df %>%
-pivot_longer(
-cols =-network, # Exclude column
-names_to ="metric", # Name of new column for the metrics
-values_to ="value"# Name of new column for the values
- ) %>%
-filter(!metric %in%c("linear_density","nb_nodes")) %>%
-mutate(value =round(value, 3))
-
-
-metric_titles <-c(density ="Density", nb_edges ="Number of Collaborations", nb_communities ="Number of Communities", nb_mean_nodes_per_community ="Mean Number of Authors per Community")
-
-# Créer le graphe avec ggplot
-density_plot <-ggplot(density_long, aes(x = network, y = value)) +
-geom_line(aes(group = metric), color ="steelblue") +
-geom_point(color ="steelblue") +
-labs(title ="",
-x ="Networks",
-y ="Value") +
-facet_wrap(~ metric, scales ="free_y", labeller =labeller(metric = metric_titles)) +
-theme(plot.title =element_text(hjust =0.5)) +
-theme_bw()
-
-
-ggsave("images/density_networks.png", density_plot, width =14, height =8, dpi =300)
-
-ggplotly(density_plot)
-```
-
-## Communities
-
-### Pareto chart of citations per community
-
-```{r}
-#| label: communities
-#| fig.width: 16
-#| fig.height: 10
-#| fig-dpi: 300
-
-# Let's look at the communities of the network 1996-2023
-
-top5_communities <- df_1996_2023 %>%
-group_by(community) %>%
- dplyr::summarise(n =n()) %>%
-arrange(desc(n)) %>%
-head(5)
-
-
-# Compute the number of community per network
-filtered_df <- df_1996_2023 %>%
-filter(community %in% top5_communities$community)
-
-#print("The most important communities in terms of number of authors are :")
-top5_communities %>%gt() %>%tab_header(title ="Top 5 communities in terms of number of authors") %>%cols_label(community ="Community", n ="Number of authors")
-
-```
-
-```{r}
-#| label: communities-2
-#| fig.width: 16
-#| fig.height: 10
-#| fig-dpi: 300
-#| fig.align: center
-#| column: screen-inset-right
-
-# Compute the sum of citations per community
-citations_per_community <- df_1996_2023 %>%
-group_by(community) %>%
- dplyr::summarise(citations =sum(citations)) %>%
-arrange(-citations) %>%
- dplyr::mutate(cumulative_citations =cumsum(citations),
-percentage = cumulative_citations /sum(citations) *100)
-
-citations_per_community %>%datatable(rownames =FALSE, extensions ='Buttons', options =list(dom ='Bfrtip', buttons =c('copy', 'csv', 'excel', 'pdf', 'print')))
-
-# No ticks and labels
-ggplot(citations_per_community, aes(x =reorder(community, -citations), y = citations)) +
-geom_bar(stat ="identity", fill ="steelblue") +
-geom_point(aes(y = cumulative_citations), group =1, color ="darkred", size =1) +
-scale_y_continuous(
-name ="Cumulative number of citations",
-sec.axis =sec_axis(~./max(citations_per_community$cumulative_citations) *100, name ="Cumulative percentage")
- ) +
-geom_hline(yintercept =max(citations_per_community$cumulative_citations) *0.25, linetype ="dashed", color ="black") +
-geom_hline(yintercept =max(citations_per_community$cumulative_citations) *0.50, linetype ="dashed", color ="black") +
-geom_hline(yintercept =max(citations_per_community$cumulative_citations) *0.75, linetype ="dashed", color ="black") +
-theme_bw() +
-theme(
-panel.border =element_blank(),
-panel.grid.major =element_blank(),
-panel.grid.minor =element_blank(),
-axis.line =element_line(colour ="black"),
-axis.ticks.x =element_blank(), # Supprimer les marques de graduation sur l'axe des x
-axis.text.x =element_blank() # Rendre les étiquettes de l'axe des x invisibles
- ) +
-labs(
-title ="Pareto chart of citations per community",
-x ="Community",
-y ="Number of Citations"
- )
-
-ggsave("images/pareto_chart_ggplot_no_xtickslabels.png", width =20, height =10, dpi =300)
-
-```
-
-### Proportion of the top 5 communities in terms of citations and authors
-
-```{r}
-#|label: gt
-# Sum of all citations from filtered_df for each community of the top 5
-top5_communities_citations <- filtered_df %>%
-group_by(community) %>%
- dplyr::summarise(citations =sum(citations)) %>%
-arrange(desc(citations))
-
-sum_citations_df_1996_2023 <-sum(df_1996_2023$citations)
-
-# Compute the proportion of citations and number of authors for each community
-top5_communities_citations <- top5_communities_citations %>%
-left_join(top5_communities, by ="community") %>%
- dplyr::mutate(prop_citations = citations / sum_citations_df_1996_2023) %>%
- dplyr::mutate(prop_authors = n /nrow(df_1996_2023)) %>%
- dplyr::mutate(cumul_prop_citations =round(cumsum(prop_citations) *100,2)) %>%
- dplyr::mutate(cumul_prop_authors =round(cumsum(prop_authors) *100,2)) %>%
- dplyr::mutate(prop_citations =round(prop_citations *100,2)) %>%
- dplyr::mutate(prop_authors =round(prop_authors *100,2)) %>%
-arrange(desc(citations))
-
-gt_top5_communities_citations <- top5_communities_citations %>%
-gt() %>%
-tab_header(title ="Proportion of citations and authors for each community") %>%
-cols_label(community ="Community",
-citations ="Number of citations",
-n ="Number of authors",
-prop_citations ="Proportion of citations (in %)",
-prop_authors ="Proportion of authors (in %)",
-cumul_prop_citations ="Cumulative proportion of citations (in %)",
-cumul_prop_authors ="Cumulative proportion of authors (in %)") %>%
-cols_align(align ="center", columns =everything())
-
-# save gt in png
-#gtsave(gt_top5_communities_citations, "images/top5_communities_citations.png")
-#gtsave(gt_top5_communities_citations, filename = "top5_communities_citations.png")
-
-
-gt_top5_communities_citations
-#pareto.chart(citations_per_community$citations, ylab = "Cumulative proportion of citations", col = "steelblue", main = "Pareto chart of the citations per community")
-
-```
-
-```{r}
-#| label: regression
-
-#model <- lm(citations ~ inverse_burt_weighted + closeness + community + color, data = df_0_2023)
-#summary(model)
-#plot(density(df_0_2023$citations), main = "Density Plot of Citations", xlab = "Citations")
-#hist(df_0_2023$citations, main = "Distribution of Citations", xlab = "Citations")
-
-#shapiro.test(residuals(model))
-
-#plot(model, which = 1)
-
-#stargazer::stargazer(model, type = "text")
-
-#library(MASS)
-
-#model_negbin <- glm.nb(citations ~ inverse_burt_weighted + closeness + community + color, data = df_0_2023)
-
-#summary(model_negbin)
-#stargazer::stargazer(model_negbin, type = "text")
-
-```
-
-```{r}
-#| label: density-citations
-#| fig.width: 10
-#| fig.height: 6
-#| fig.align: center
-#| eval: false
-#| echo: false
-
-df_0_2023 %>%
-ggplot(aes(x =log(citations +1),
-y = ..density..)) +
-geom_histogram(bins =10, fill ="lightblue", color ="black") +
-geom_density(color ="black",
-size =1)+
-theme_bw() +
-labs(title ="Distribution of citations (log scale)", x ="Citations", y ="Frequency")+
-theme(plot.title =element_text(hjust =0.5))+
-theme(axis.text.x =element_text(size =12),
-axis.text.y =element_text(size =12),
-axis.title.x =element_text(size =14),
-axis.title.y =element_text(size =14),
-plot.title =element_text(size =16, face ="bold"))
-
-
-
-ggplot(df_0_2023, aes(x = citations,
-y = ..density..)) +
-geom_histogram(binwidth =10, fill ="lightblue", color ="black") +
-geom_density(color ="black",
-size =0.5)+
-labs(title ="Distribution of citations", x ="Citations", y ="Frequency") +
-theme_bw()
-```
+
---
+title: "Systematic Literature Review"
+date: "today"
+date-format: long
+author:
+ - name: Olivier Caron
+ email: olivier.caron@dauphine.psl.eu
+ affiliations:
+ name: "Paris Dauphine - PSL"
+ city: Paris
+ state: France
+ - name: Christophe Benavent
+ email: christophe.benavent@dauphine.psl.eu
+ affiliations:
+ name: "Paris Dauphine - PSL"
+ city: Paris
+ state: France
+toc: true
+number-sections: true
+number-depth: 10
+format:
+ html:
+ theme:
+ light: yeti
+ #dark: darkly
+ code-fold: true
+ code-summary: "Display code"
+ code-tools: true #enables to display/hide all blocks of code
+ code-copy: true #enables to copy code
+ grid:
+ body-width: 1000px
+ margin-width: 100px
+ toc: true
+ toc-location: left
+execute:
+ echo: true
+ warning: false
+ message: false
+editor: visual
+fig-align: "center"
+highlight-style: ayu
+css: styles.css
+reference-location: margin
+bibliography: slrbib.bib
+---
+
+# Introduction
+
+```{r}
+#| label : libraries-data-r
+library(ape)
+library(cowplot)
+library(DT)
+library(ggcorrplot)
+library(ggplot2)
+library(ggrepel)
+library(ggthemes)
+library(ggwordcloud)
+library(gt)
+library(heatmaply)
+library(Hmisc)
+library(plotly)
+library(quanteda)
+library(quanteda.textmodels)
+library(quanteda.textstats)
+library(RColorBrewer)
+library(reactable)
+library(reticulate)
+library(Rtsne)
+library(splitstackshape)
+library(tidyr)
+library(webshot)
+library(wesanderson)
+library(word2vec)
+
+theme_set(theme_minimal())
+
+library(tidyverse)
+df <-read_delim("nlp_full_data_final_18-08-2023.csv",delim =";",
+escape_double =FALSE, trim_ws =TRUE) %>%
+select(1,5,7,8, 14, 15,16,21,23,32,33,39,40,51,52,53) %>%
+group_by(entry_number) %>%
+filter(row_number()==1) %>%
+rename(id=1,title =8, review=9, text=10, keywords =13)
+
+df$review2<-ifelse(str_detect(df$review,"Proceedings")==TRUE, "out", df$review)
+df$review2<-ifelse(str_detect(df$review2, "Conference")==TRUE, "out", df$review2)
+df$review2<-ifelse(str_detect(df$review2, "Transactions")==TRUE, "out", df$review2)
+
+# for the networks
+list_articles <-read_csv2("nlp_full_data_final_18-08-2023.csv") %>%
+select(1,5,7,8, 14, 15,16,21,23,32,33,39,40,51,52,53) %>%
+rename(id=1,title =8, review=9, text=10, keywords =13)
+
+list_articles$review2<-ifelse(str_detect(list_articles$review,"Proceedings")==TRUE, "out", list_articles$review)
+list_articles$review2<-ifelse(str_detect(list_articles$review2, "Conference")==TRUE, "out", list_articles$review2)
+list_articles$review2<-ifelse(str_detect(list_articles$review2, "Transactions")==TRUE, "out", list_articles$review2)
+list_articles <- list_articles %>%filter(review2 !="out")
+
+```
+
+```{python}
+#| label: libraries-data-python
+
+import community as community_louvain
+import pandas as pd
+import numpy as np
+import networkx as nx
+import matplotlib.pyplot as plt
+import plotly.express as px
+import re
+
+from ipysigma import Sigma, SigmaGrid
+from pyvis.network import Network
+from itertools import combinations
+from datetime import datetime
+from functools import partial
+
+list_articles = r.list_articles
+```
+
+# Description
+
+```{r}
+#| label: description
+#| fig-height: 6
+#| fig-width: 14
+#| column: screen-inset-right
+
+
+df<- df%>%filter(review2 !="out")
+t0 <-as.data.frame(table(df$review2))%>%
+filter(Freq>3)
+
+g01 <-ggplot(t0, aes(x=reorder(Var1, Freq), y=Freq)) +
+geom_bar(stat="identity", fill="steelblue") +
+coord_flip() +
+theme_minimal() +
+labs(title="Number of Articles per Journal", y="Proportion", x="") +
+theme(
+axis.title.y =element_blank(),
+axis.text.y =element_text(size =10)
+ )
+
+t1<-as.data.frame(table(df$year))
+
+# Graph 2: Number of publications per year
+g02 <-ggplot(t1, aes(x=Var1, y=Freq, group=1)) +
+geom_line(size=1.1, color="steelblue") +
+geom_point(size=2, color="steelblue") +
+geom_smooth(color="#7D7C7C", linewidth=0.5)+
+theme_minimal() +
+labs(title="Number of Publications per Year", y="", x="Year") +
+theme(
+axis.text.x =element_text(angle =45, hjust =1, size =10),
+axis.title.x =element_blank(),
+plot.title =element_text(hjust =0.5)
+ )
+
+plot_grid(g01, g02, labels =c('A', 'B'), label_size =8, ncol=2, rel_widths =c(1,1))
+plotgrid <-plot_grid(g01, g02, labels =c('A', 'B'), label_size =8, ncol=2, rel_widths =c(1,1))
+
+ggsave(filename="./images/quant.jpeg", plotgrid, width =40, height =18, units ="cm")
+
+```
+
+# Keywords
+
+```{r}
+#| label: keywords1
+#| fig-height: 5
+#| fig-width: 13
+#| column: screen-inset-right
+
+user2<- df %>%
+select(id, keywords)%>%
+mutate(keywords=tolower(keywords))%>%
+separate(keywords, sep="\\|", into=c("A1","A2","A3","A4","A5","A6","A7","A8","A9","A10", "A11", "A12", "A13", "A14", "A15", "A16"))%>%
+pivot_longer(-id, names_to="Rang", values_to ="keywords") %>%
+filter(!is.na(keywords))
+
+test_user2 <- df %>%
+select(id, keywords) %>%
+mutate(keywords =tolower(keywords)) %>%
+cSplit('keywords', sep ="|", direction ="wide") %>%
+pivot_longer(-id, names_to ="Rang", values_to ="keywords", values_drop_na =TRUE)
+
+
+user2$keywords=str_trim(user2$keywords,side ="both")
+
+user2$keywords[user2$keywords=="artificial intelligence (ai)"]<-"ai"
+user2$keywords[user2$keywords=="automated analysis of text"]<-"automated text analysis"
+user2$keywords[user2$keywords=="automated textual analysis"]<-"automated text analysis"
+user2$keywords[user2$keywords=="natural language processinf"]<-"nlp"
+user2$keywords[user2$keywords=="natural language processing (nlp)"]<-"nlp"
+user2$keywords[user2$keywords=="natural language processing (nlp)-based approach"]<-"nlp"
+user2$keywords[user2$keywords=="nlp tools"]<-"nlp"
+user2$keywords[user2$keywords=="natural language processing"]<-"nlp"
+user2$keywords[user2$keywords=="online review"]<-"online reviews"
+user2$keywords[user2$keywords=="online shopping review"]<-"online reviews"
+user2$keywords[user2$keywords=="review"]<-"online reviews"
+user2$keywords[user2$keywords=="reviews"]<-"online reviews"
+user2$keywords[user2$keywords=="topic modelling"]<-"topic modeling"
+user2$keywords[user2$keywords=="user generated content"]<-"user-generated content"
+
+# compter les mots cles
+foo<- user2 %>%
+mutate(n=1) %>%
+group_by(id,keywords)%>%
+summarise(n=sum(n))
+
+foo1<- foo %>%
+group_by(keywords)%>%
+summarise(m=n())%>%
+filter(m>1)
+
+set.seed(42)
+ggplot(foo1, aes(label = keywords, size = m)) +
+geom_text_wordcloud() +
+scale_size_area(max_size =10) +
+theme_minimal()
+
+ggsave(filename="./images/keywords01.jpeg", plot=last_plot(), width =27, height =18, units ="cm")
+
+# tsne projection
+
+foo2<-user2 %>%
+select(-Rang)%>%
+group_by(id,keywords)%>%
+summarize(n=n()) %>%
+pivot_wider(id,names_from ="keywords", values_from ="n" )
+
+foo2<- foo2 %>%
+replace(is.na(.),0) %>%
+ dplyr::select(-id)
+
+
+foo3<-t(foo2)
+tsne_out <-Rtsne(foo3,perplexity =10, dim=2, check_duplicates =FALSE) # Run TSNE
+tsne_out2<-as.data.frame(tsne_out$Y)
+keywords<-rownames(foo3)
+library(ggrepel)
+tsne_out3<-cbind(tsne_out2, keywords)
+tsne_out3<-merge(tsne_out3,foo1)
+tsneplot <- tsne_out3%>%
+filter(m>0) %>%
+ggplot(aes(x=V1, y=V2, label=keywords))+
+geom_point(aes(size=m), alpha=.5)+
+geom_text_repel(aes(label=ifelse(m>1,keywords,""),size=log(m)), max.overlaps =50)+
+scale_size_continuous(range =c(2, 10))
+
+tsneplot
+ggsave(filename="./images/keywords.jpeg", tsneplot, width =27, height =18, units ="cm")
+```
+
+## With a breakdown of the keywords
+
+The strategy is to reduce the morphologies of the most frequent keywords, in the form of unigrams, and then re-decompose the terms that make up the non-recoded keywords.
+
+```{r}
+#| label: keywords2
+#| fig-height: 5
+#| fig-width: 15
+#| column: screen-inset-right
+
+key1<- df %>%
+select(id, keywords)%>%
+mutate(keywords=tolower(keywords))%>%
+separate(keywords, sep="\\|", into=c("A1","A2","A3","A4","A5","A6","A7","A8","A9","A10", "A11", "A12", "A13", "A14", "A15", "A16"))%>%
+pivot_longer(-id, names_to="Rang", values_to ="keywords") %>%
+filter(!is.na(keywords)) %>%
+mutate(keywords=str_squish(keywords),
+keywords=str_replace(keywords, "text mining", "textmining"),
+keywords=str_replace(keywords, "text-mining", "textmining"),
+keywords=str_replace(keywords, "text analysis", "textanalysis"),
+keywords=str_replace(keywords, "text-analysis", "textanalysis"),
+keywords=str_replace(keywords, "user-generated content", "ugc"),
+keywords=str_replace(keywords, "user-generated content-ugc", "ugc"),
+keywords=str_replace(keywords, "user generated content", "ugc"),
+keywords=str_replace(keywords, "user genrated content (ugc)", "ugc"),
+keywords=str_replace(keywords, "artificial intelligence", "ai"),
+keywords=str_replace(keywords, "artificial intelligence", "ai"),
+keywords=str_replace(keywords, "artificial intelligence (ai)", "ai"),
+keywords=str_replace(keywords, "artificial neural network", "ai"),
+keywords=str_replace(keywords, "artificial neural networks", "neural network"),
+keywords=str_replace(keywords, "natural language processing", "nlp"),
+keywords=str_replace(keywords, "natural language processing (nlp)", "nlp"),
+keywords=str_replace(keywords, "natural language processing (nlp)-based approach", "nlp"),
+keywords=str_replace(keywords, "natural language processing (nlp)-based approach", "nlp"),
+keywords=str_replace(keywords, "topic model analysis", "topics"),
+keywords=str_replace(keywords, "topic modeling", "topics"),
+keywords=str_replace(keywords, "topic modelling", "topics"),
+keywords=str_replace(keywords, "topic model", "topics"),
+keywords=str_replace(keywords, "e wom", "ewom"),
+keywords=str_replace(keywords, "e-wom", "ewom"),
+keywords=str_replace(keywords, "electronic word-of-mouth", "ewom"),
+keywords=str_replace(keywords, "electronic word of mouth", "ewom"),
+keywords=str_replace(keywords, "electronic word of mouth (ewom)", "ewom"),
+keywords=str_replace(keywords, "online word-of-mouth", "ewom"),
+keywords=str_replace(keywords, "online word of mouth", "ewom"),
+keywords=str_replace(keywords, "online word of mouth", "ewom"),
+keywords=str_replace(keywords, "negative word-of-mouth nwom", "ewom"),
+keywords=str_replace(keywords, "word-of-mouth communication", "ewom"),
+keywords=str_replace(keywords, "word-of-mouth", "ewom"),
+keywords=str_replace(keywords, "online review", "reviews"),
+keywords=str_replace(keywords, "online reviews", "reviews"),
+keywords=str_replace(keywords, "social media", "socialmedia"),
+keywords=str_replace(keywords, "latent dirichlet allocation", "lda"),
+keywords=str_replace(keywords, "latent dirichlet allocation (lda)", "lda"),
+keywords=str_replace(keywords, "latent dirichlet allocation algorithm", "lda"),
+keywords=str_replace(keywords, "word embedding", "embedding"),
+keywords=str_replace(keywords, "word2vec", "embedding"),
+keywords=str_replace(keywords, "latent dirichlet allocation model", "lda"),
+keywords=str_replace(keywords, "machine learning", "ml"),
+keywords=str_replace(keywords, "machine learning and linguistic analysis", "ml"),
+keywords=str_replace(keywords, "big data", "bigdata"),
+keywords=str_replace(keywords, "liwc", "liwc"),
+keywords=str_replace(keywords, "linguistic inquiry and word count liwc", "liwc"),
+keywords=str_replace(keywords, "bibliometrics", "bibliometric"),
+keywords=str_replace(keywords, "\\(", ""),
+keywords=str_replace(keywords, "\\)", "")
+ )
+foo<-key1%>%group_by(keywords)%>%
+summarize(n=n())
+foo
+
+key2<- key1 %>%
+select(-Rang) %>%
+separate(keywords, sep=" ", into=c("A1","A2","A3","A4","A5","A6","A7","A8","A9","A10", "A11", "A12", "A13", "A14", "A15", "A16")) %>%
+pivot_longer(-id, names_to="Rang", values_to ="keywords") %>%
+mutate(n=nchar(keywords)) %>%
+filter(!is.na(keywords))%>%
+filter(n>0) %>%select(-n)
+
+key3<-key2%>%
+group_by(keywords)%>%
+summarize(k=n())
+
+foo2<-key2 %>%
+group_by(id,keywords)%>%
+summarize(m=n()) %>%
+pivot_wider(id,names_from ="keywords", values_from ="m", names_repair="unique" )
+
+foo2<- foo2 %>%
+replace(is.na(.),0) %>%
+ dplyr::select(-1)
+
+
+foo3<-t(foo2)
+tsne_out <-Rtsne(foo3,perplexity =25, dim=2, check_duplicates =FALSE) # Run TSNE
+tsne_out2<-as.data.frame(tsne_out$Y)
+keywords<-rownames(foo3)
+library(ggrepel)
+tsne_out3<-cbind(tsne_out2, keywords)
+tsne_out3<-merge(tsne_out3,key3)
+
+
+tsne_out3%>%
+ggplot(aes(x=V1, y=V2, label=keywords))+
+geom_text_repel(aes(label=ifelse(k>4,keywords,""),size=k), max.overlaps =50)+
+scale_size_continuous(range =c(1, 10))
+
+
+ggsave(filename="./images/keywords03.jpeg", plot=last_plot(), width =27, height =18, units ="cm")
+
+
+```
+
+```{r}
+#| label: keywords3
+#| fig-height: 3
+#| fig-width: 10
+#| column: screen-inset-right
+
+foo<- df%>%select(id,year)
+
+foo5<-key2 %>%
+group_by(id,keywords)%>%
+summarize(m=n()) %>%
+left_join(foo) %>%
+group_by(year,keywords)%>%
+summarise(m=sum(m))%>%
+left_join(key3)%>%
+filter(keywords=="ai"| keywords=="textmining"| keywords=="nlp"| keywords=="topics"|
+ keywords=="lda"| keywords=="ml"|keywords=="embbeding"|keywords=="liwc")
+
+ggplot(foo5, aes(x=year, y=m, group=keywords))+
+geom_area(aes(fill=keywords))+
+scale_fill_manual(values =rev(wes_palette("Zissou1", n =length(unique(foo5$keywords)), type ="continuous")))+#nice palette
+scale_x_continuous(breaks =unique(foo5$year)) #show every year on x axis
+
+```
+
+```{r}
+#| label: keywords4
+#| fig-height: 3
+#| fig-width: 10
+#| column: screen-inset-right
+
+foo5<-key2 %>%
+group_by(id,keywords)%>%
+summarize(m=n()) %>%
+left_join(foo) %>%
+group_by(year,keywords)%>%
+summarise(m=sum(m))%>%
+left_join(key3)%>%
+filter(keywords=="ewom"| keywords=="reviews"| keywords=="socialmedia"| keywords=="ugc"| keywords=="bibliometric")
+
+ggplot(foo5, aes(x=year, y=m, group=keywords))+
+geom_area(aes(fill=keywords))+
+scale_fill_manual(values =rev(wes_palette("Zissou1", n =length(unique(foo5$keywords)), type ="continuous")))+
+scale_x_continuous(breaks =unique(foo5$year)) #show every year on x axis
+```
+
+
+# Structural topic model (STM)
+
+## Data preparation
+
+```{r}
+#| label: data-preparation
+
+library(quanteda)
+library(stm)
+df$Texte<-paste(df$title, " . ", df$text, df$keywords)
+
+df$year<-ifelse(df$year<2011,2010,
+ifelse(df$year>2010& df$year<2016,2015,
+ifelse(df$year>2015& df$year<2020,2019,df$year)))%>%
+as.character()
+table(df$year)
+corpus <- quanteda::corpus(
+ df,
+text_field ="Texte",
+docid_field ="id",
+unique_docnames =FALSE
+ )
+
+corpus_dfm <-
+ quanteda::dfm(corpus) %>%
+ quanteda::dfm_trim(
+# tokens_remove = 'nan',
+# Get rid of any word that doesn't occur at least x
+min_termfreq =5,
+# Get rid of any word that is in at least x per cent of documents
+max_docfreq =0.9,
+# Below we specify percentages - we can specify counts or ranks
+docfreq_type ="prop"
+ ) %>%
+ quanteda::dfm_keep(
+# '[a-zA-Z]', # filter out terms without alpha
+# valuetype='regex',
+min_nchar =2) # Only words with at least 3 characters
+
+out <- quanteda::convert(corpus_dfm, to ='stm')
+
+
+K =16# number of topics
+
+FORMULA_RANGE <-paste("1",as.character(K), sep=":")
+PREVALENCE_FORMULA <-as.formula(paste("~", "year"))
+PREVALENCE_FORMULA_EFFECT <-as.formula( paste(FORMULA_RANGE, paste("~ ", "year", collapse="+") ) )
+
+OUTPUT_PATH <-"./"
+OUTPUT_FILE <-sprintf("STM_model_%s.RData", K)
+
+message(PREVALENCE_FORMULA)
+message(PREVALENCE_FORMULA_EFFECT)
+message(OUTPUT_FILE)
+
+```
+
+## Estimation
+
+```{r}
+#| label: estimation
+
+t1=Sys.time()
+stm_model <- stm::stm(
+documents = out$documents,
+vocab = out$vocab,
+data = out$meta,
+K = K,
+prevalence = PREVALENCE_FORMULA,
+verbose =TRUE, # show progress
+init.type ="Spectral",
+seed =56,
+emtol =1e-05,
+max.em.its=40
+ )
+t2=Sys.time()
+
+
+stm_effects <- stm::estimateEffect(PREVALENCE_FORMULA_EFFECT,
+stmobj = stm_model, metadata = out$meta)
+
+saveRDS(stm_model, "stm_model.rds")
+saveRDS(stm_effects, "stm_effects.rds")
+
+
+stm_model<-readRDS("stm_model.rds")
+stm_effects<-readRDS("stm_effects.rds")
+
+labelTopics(stm_model, topics =NULL, n =5, frexweight =0.5)
+
+```
+
+## Topics' description (beta)
+
+4 indicators: probability, exclusivity, resulting frex, lift and score.
+
+$FREX = \left(\frac{w}{F} + \frac{1-w}{E}\right)^{-1}$
+
+see lift:
+
+$Lift = \beta_{k,v}/(w_v/\sum_v w_v)$
+
+the score:
+
+$\beta_{v, k} (\log \beta_{w,k} - 1 / K \sum_{k'} \log \beta_{v,k'})$
+
+```{r}
+#| label: topics-description
+#| fig-height: 7
+#| fig-width: 12
+#| column: screen-right
+#| fig-align: center
+beta<-t(stm_model$beta$logbeta[[1]]) %>%
+as.data.frame()
+
+logbeta<-stm_model$beta$logbeta[[1]]
+
+vocab<-stm_model$vocab %>%
+as.data.frame()
+
+vocab<- vocab %>% dplyr::rename(feature=1)
+
+#frequence
+
+beta2<-cbind(vocab, beta) %>%
+pivot_longer(-feature, names_to="topic", values_to ="logprob") %>%
+ dplyr::group_by(topic)%>%
+ dplyr::mutate(r=rank(desc(logprob)),
+p=exp(logprob)) %>%
+ dplyr::filter(r<31)
+
+#labelisation des topics
+
+beta3<-calcfrex(logbeta, w =0.25, wordcounts =NULL)
+
+beta4<-cbind(vocab, beta3) %>%
+as.data.frame() %>%
+pivot_longer(-feature, names_to="topic2", values_to ="frex") %>%
+mutate(topic=paste0("V",topic2))
+
+
+beta2<- beta2 %>%
+left_join(beta4, by=c("feature", "topic"))
+
+synth<-labelTopics(stm_model, topics =NULL, n =5, frexweight =0.25)
+prob<-synth$prob
+frex<-synth$frex
+
+#intitulés suggérés par Bard
+
+beta2$topic[beta2$topic=="V1"]<-"T01 Analyse bibliométrique\ndes recherches marketing"
+beta2$topic[beta2$topic=="V2"]<-"T02 Qualité du service en ligne"
+beta2$topic[beta2$topic=="V3"]<-"T03 Publicité en ligne et contenu personnalisé"
+beta2$topic[beta2$topic=="V4"]<-"T04 Personnalisation du produit et régulation"
+beta2$topic[beta2$topic=="V5"]<-"T05 Consumer exp dictionnary "#il a du mal
+beta2$topic[beta2$topic=="V6"]<-"T06 Avis en ligne et sentiment "
+beta2$topic[beta2$topic=="V7"]<-"T07 Customer online review and satisfaction"
+beta2$topic[beta2$topic=="V8"]<-"T08 Tourism"
+beta2$topic[beta2$topic=="V9"]<-"T09 Decoding Guest Satisfaction Through Airbnb Reviews"
+beta2$topic[beta2$topic=="V10"]<-"T10 Social Media Analysis"
+beta2$topic[beta2$topic=="V11"]<-"T11 Specific Macau"
+beta2$topic[beta2$topic=="V12"]<-"T12 Online Brand Strategy"
+beta2$topic[beta2$topic=="V13"]<-"T13 Text Data for Marketing Decisions"
+beta2$topic[beta2$topic=="V14"]<-"T14 Social Media Engagement"
+beta2$topic[beta2$topic=="V15"]<-"T15 Text Analytics in Market Segmentation"
+beta2$topic[beta2$topic=="V16"]<-"T16 Ewom"
+
+
+#la transparence rend compte de l'exclusitivité frex
+stm_topics <-ggplot(beta2)+
+geom_text_wordcloud(aes(label=feature, size=p, alpha=2100-frex), color="darkblue")+
+facet_wrap(vars(topic), ncol=4)+
+scale_size_area(max_size =7)+
+theme(strip.text =element_text(size =10))
+
+ggsave(paste0("./images/stm_topic",K,".jpg"),
+ stm_topics,
+width =28,
+height =20,
+units ="cm",
+dpi =300)
+
+
+```
+
+## Prevalence description (beta)
+
+```{r}
+#| label: prevalence-description
+#| fig-height: 5
+#| fig-width: 10
+#| fig-align: center
+
+foo<-stm_effects$parameters
+
+library(tidyr)
+library(plyr)
+
+#pour separer les liste et les transformer en df
+#attention ça pertube
+foo1<-ldply(foo, data.frame)%>%
+select(contains('est'))
+
+library(dplyr)
+
+
+F<-data.frame(param=c("cte", "2015", "2019","2020","2021","2022","2023"))
+
+
+# autant de fois que K
+param<-rbind(F, F, F, F, F, F, F, F, F, F,
+ F, F, F, F, F ,F)
+
+
+R<-data.frame(topic=c(
+"T01 bibliometrics consumer",
+"T02 Service quality review",
+"T03 Ads and privacy",
+"T04 Customization",
+"T05 Consumer exp dictionnary ",
+"T06 Product review sentiment ",
+"T07 customer online review",
+"T08 Tourism",
+"T09 Airbnb",
+"T10 Social media",
+"T11 specific",
+"T12 Branding",
+"T13 marketing analytics",
+"T14 engagement media sociaux",
+"T15 marketing strategy",
+"T16 e wom"
+))
+
+#autant de fois que de paramètres
+R<-rbind(R, R, R, R, R,
+ R, R ) %>%arrange(topic)
+
+
+
+foo2<-cbind(foo1, R, param)%>%
+pivot_longer(-c("topic","param"), names_to ="sim", values_to ="est") %>%
+ dplyr::group_by(topic,param)%>%
+ dplyr::summarise(mean=mean(est, na.rm=TRUE),
+se =sd(est)/sqrt(24),
+t=abs(mean)/se) %>%
+ dplyr::mutate(param=ifelse(param=="cte", "2010", param))
+
+foo3<-cbind(foo1, R, param)%>%
+pivot_longer(-c("topic","param"), names_to ="sim", values_to ="est") %>%
+ dplyr::group_by(topic,param)%>%
+ dplyr::summarise(mean=mean(est, na.rm=TRUE),
+se =sd(est)/sqrt(24),
+t=abs(mean)/se) %>%
+ dplyr::filter(param=="cte")%>%
+ dplyr::select(-2,-4,-5) %>%
+ dplyr::rename(cte = mean)
+
+foo4<-foo2 %>%left_join(foo3) %>%
+mutate(mean2=ifelse(param!="2010",mean+cte,mean))
+
+
+# dplyr::mutate(time=ifelse(str_sub(param, 1,1)=="0" |str_sub(param, 1,1)=="1" , "no", "yes"))
+
+
+
+
+
+stm_effect <-ggplot(foo4, aes(x=param,y=mean2,group=topic))+
+geom_bar(stat="identity")+
+# geom_smooth(se = FALSE )+
+facet_wrap(vars(topic), scale="fixed",ncol =4)+
+theme(axis.text.y =element_text(size=5))+
+theme(axis.text.x =element_text(size=5))+
+labs(x=NULL, y="prévalence")
+
+stm_effect
+
+
+ggsave(paste0("./images/stm_effect",K,".jpg"),
+ stm_effect,
+width =28,
+height =20,
+units ="cm",
+dpi =300)
+
+
+```
+
+## The law of entropy
+
+```{r}
+#| label: entropy
+#| fig-height: 5
+#| fig-width: 9
+#| fig-align: center
+nb.cols <-16
+mycolors <-colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
+
+
+topics_plot <-ggplot(foo4, aes(x=param,y=mean2,group=topic))+
+geom_bar(stat="identity", aes(fill=topic))+
+# geom_smooth(se = FALSE )+
+theme(axis.text.y =element_text(size=5))+
+theme(axis.text.x =element_text(size=5))+
+labs(x=NULL, y="prévalence")+
+scale_fill_manual(values = mycolors)
+
+ggplotly(topics_plot)
+
+
+#entropie
+
+foo5<-foo4%>%select(1,2,7)%>%
+mutate(e=mean2*log(mean2)) %>%
+group_by(param) %>%
+ dplyr::summarise(entropie=-sum(e))
+
+foo5$param<-as.numeric(foo5$param)
+ggplot(foo5,aes(x=param, y=entropie, group =1))+
+geom_point(stat ="identity")+geom_smooth(se=FALSE)+ylim(1.5,3)
+
+
+```
+
+# Networks of authors
+
+## Check name of authors
+
+We need to check if there are more than one unique `authorname` per `authid`. If so, we need to change the different names of author to the same name in order to have the exact same node per author later in the network.
+
+```{r}
+#| label: check-name-authors
+
+test <- list_articles %>%
+group_by(authid) %>%
+select(authid, authname, id) %>%
+ dplyr::mutate(n =n()) %>%
+ ungroup
+
+result <- test %>%
+group_by(authid) %>%
+filter(n_distinct(authname) >1) %>%
+distinct(authid, .keep_all =TRUE)
+
+result %>%reactable()
+
+number_duplicates <-nrow(result)
+
+cat("There are ", number_duplicates, " authors registered with different names.")
+```
+
+## Correct the duplicate names
+
+Let's correct that by using one property of the distinct function: the `.keep_all = TRUE` parameter. It keeps the first occurrence of each group, which is the first row encountered for each unique combination of `authid` and `authname`. It will be faster than manually changing the name of each author.
+
+```{r}
+#| label: change-name-authors
+
+# Merge list_articles with result on the authid column
+merged_df <-left_join(list_articles, result, by ="authid")
+
+# Replace authname values in list_articles with those from result
+list_articles$authname <-ifelse(!is.na(merged_df$authname.y), merged_df$authname.y, list_articles$authname)
+
+cat("There are", n_distinct(list_articles$id), "articles and", n_distinct(list_articles$authname), "authors overall in the data.")
+
+# Write the updated dataframe to a CSV file
+write_csv2(list_articles, "nlp_full_data_final_unique_author_names.csv")
+```
+
+It is now done. We can check again if there are more than one unique `authorname` per `authid`.
+
+## Verification of duplicate names
+
+```{r}
+#| label: check-unique-name-authors
+
+test <- list_articles %>%
+group_by(authid) %>%
+select(authid, authname, id) %>%
+ dplyr::mutate(n =n())
+
+result <- test %>%
+group_by(authid) %>%
+filter(n_distinct(authname) >1) %>%
+distinct(authid, .keep_all =TRUE) %>%
+relocate(id)
+
+result %>%reactable()
+
+```
+
+## Construct the networks
+
+```{python}
+#| label: function-network
+
+# Constants to include in the network as hovering text for nodes
+COLUMNS_TO_COLLECT = [
+'affilname', 'affiliation_country', 'title', 'review', 'year', 'citedby_count', 'subjects_area', 'keywords'
+]
+
+def get_author_info(filtered_articles, columns):
+"""
+ Given a DataFrame of filtered articles and a list of column names,
+ this function collects author information and returns it as a dictionary.
+ """
+ author_info = {col: {} for col in columns}
+ author_info["citations"] = {}
+
+for _, row in filtered_articles.iterrows():
+ author_name = row['authname']
+
+if pd.notna(row['citedby_count']):
+ author_info["citations"][author_name] = author_info["citations"].get(author_name, 0) + row['citedby_count']
+
+for col in columns:
+ value = row[col]
+if pd.notna(value):
+ value =str(value).strip()
+if author_name in author_info[col]:
+if value notin author_info[col][author_name]:
+ author_info[col][author_name] +=" | "+ value
+else:
+ author_info[col][author_name] = value
+
+return author_info
+
+def sigma_graph(dataframe, start_year, end_year):
+"""
+ Creates a graph representing author collaborations based on a given DataFrame of articles.
+ Filters the articles based on the given start and end years.
+ """
+# Error handling
+if dataframe.empty:
+print("The DataFrame is empty.")
+returnNone, None
+
+for column in COLUMNS_TO_COLLECT:
+if column notin dataframe.columns:
+print(f"The DataFrame is missing the column: {column}")
+returnNone, None
+
+ list_articles = dataframe
+ filtered_articles = list_articles[(list_articles['year'] >= start_year) & (list_articles['year'] <= end_year)]
+
+ author_pairs = []
+ grouped = filtered_articles.groupby('id')[['authid', 'authname']].agg(list).reset_index()
+
+for _, row in grouped.iterrows():
+id= row['id']
+ authors = row['authid']
+ authnames = row['authname']
+
+iflen(authors) ==1:
+ author_pairs.append((id, authors[0], authors[0], authnames[0], authnames[0]))
+eliflen(authors) >1:
+ author_combinations =list(combinations(range(len(authors)), 2))
+for i, j in author_combinations:
+ author_pairs.append((id, authors[i], authors[j], authnames[i], authnames[j]))
+
+ result_df = pd.DataFrame(author_pairs, columns=['id', 'authid1', 'authid2', 'authname1', 'authname2'])
+
+ collaboration_df = result_df[["authname1", "authname2"]]
+ collaboration_df = pd.DataFrame(np.sort(collaboration_df.values, axis=1), columns=collaboration_df.columns)
+ collaboration_df['value'] =1
+ collaboration_df = collaboration_df.groupby(["authname1", "authname2"], sort=False, as_index=False).sum()
+
+ G = nx.from_pandas_edgelist(collaboration_df, 'authname1', 'authname2', edge_attr='value', create_using=nx.Graph())
+
+for u, v in G.edges:
+ G[u][v]["color"] ="#7D7C7C"
+
+for index, row in collaboration_df.iterrows():
+ G.add_edge(row['authname1'], row['authname2'], weight=row['value'])
+
+ metrics = {
+'centrality': nx.degree_centrality,
+'betweenness': nx.betweenness_centrality,
+'closeness': nx.closeness_centrality,
+'eigenvector_centrality': partial(nx.eigenvector_centrality, max_iter=1000),
+'burt_constraint_weighted': partial(nx.constraint, weight="value"),
+'burt_constraint_unweighted': nx.constraint
+ }
+
+for attr, func in metrics.items():
+ nx.set_node_attributes(G, func(G), attr)
+
+ author_info = get_author_info(filtered_articles, COLUMNS_TO_COLLECT)
+
+for col in COLUMNS_TO_COLLECT:
+ nx.set_node_attributes(G, author_info[col], col)
+
+ nx.set_node_attributes(G, author_info['citations'], 'citations')
+
+# Compute the inverse burt constraint to plot the lowest values as the biggest nodes
+# (= authors that are the less constrained in their network => multiple probable collaborations)
+for node in G.nodes:
+# Check if the 'burt_constraint_weighted' metric exists for the node
+if'burt_constraint_weighted'in G.nodes[node]:
+ burt_score = G.nodes[node]['burt_constraint_weighted']
+# Calculate the inverse, avoiding division by zero
+ G.nodes[node]['inverse_burt_weighted'] =1/ burt_score if burt_score !=0else0
+else:
+# Handle the case where the 'burt_constraint_weighted' metric is not available for this node
+# For example, by setting the value to None or a default value
+ G.nodes[node]['inverse_burt_weighted'] =None# or another default value
+
+# Compute Louvain commmunities
+ partition = community_louvain.best_partition(G)
+
+for node, comm_number in partition.items():
+ G.nodes[node]['community'] = comm_number
+
+# Color the graph using the greedy coloring algorithm with the 'largest_first' strategy
+ colors = nx.greedy_color(G, strategy='largest_first', interchange=False)
+
+# Set the computed colors as an attribute to each node in the graph
+ nx.set_node_attributes(G, colors, 'color')
+# Now, each node in the graph G has an attribute 'color' that corresponds to the color assigned by the greedy_color function
+
+# Computer HITS scores
+ hubs, authorities = nx.hits(G, max_iter=100, nstart=None, normalized=False)
+
+# Set the computed scores as an attribute to each node in the graph
+ nx.set_node_attributes(G, hubs, 'hub_score')
+ nx.set_node_attributes(G, authorities, 'authority_score')
+
+
+# Creating a list of dictionaries containing the attributes of each node
+ data_for_df = []
+for node in G.nodes(data=True):
+# `node` is a tuple (node_name, attributes_dict)
+ node_data = node[1] # Extracting the attributes dictionary
+ node_data['Node'] = node[0] # Adding the node name as an attribute
+
+# Adding the attributes dictionary of this node to the list
+ data_for_df.append(node_data)
+
+# Creating a DataFrame from the list of dictionaries
+ df_nodes = pd.DataFrame(data_for_df)
+
+ Sigma.write_html(G,
+ default_edge_type ="curve", # Default edge type
+ clickable_edges =True, # Clickable edges
+ edge_size ="value", # Set edge size
+ fullscreen =True, # Display in fullscreen
+ label_density =3, # Label density (= increase to have more labels appear at normal zoom level)
+ label_font ="Helvetica Neue", # Label font
+ max_categorical_colors =10, # Max categorical colors
+ node_border_color_from ='node', # Node border color from node attribute
+ node_color ="community", # Set node colors
+ node_label_size ="citations", # Node label size
+#node_label_size_range = (12, 36), # Node label size range
+ node_label_size_range = (12, 30), # Node label size range
+#node_metrics = {"community": {"name": "louvain", "resolution": 1}}, # Specify node metrics
+ node_size ="citations", # Node size
+#node_size_range = (3, 30), # Node size range
+ node_size_range = (2, 20), # Node size range
+ path =f"networks/authors/{start_year}_{end_year}_VF.html", # Output file path
+ start_layout =3, # Start layout algorithm
+#node_border_color = "black", # Node border color
+#edge_color = "#7D7C7C" # Edge color
+# node_label_color = "community" # Node label color
+ )
+
+
+return G, df_nodes
+```
+
+## Create the overall network (from 1996 to 2023)
+
+```{python}
+#| label: create-network-overall
+
+print("The year range of the publications is from {} to {}".format(int(list_articles['year'].min()), int(list_articles['year'].max())))
+
+#Let's try each year from 2015 as end year onwards
+
+
+G_1996_2015, df_1996_2015 = sigma_graph(list_articles, 1996, 2015)
+G_1996_2016, df_1996_2016 = sigma_graph(list_articles, 1996, 2016)
+G_1996_2017, df_1996_2017 = sigma_graph(list_articles, 1996, 2017)
+G_1996_2018, df_1996_2018 = sigma_graph(list_articles, 1996, 2018)
+G_1996_2019, df_1996_2019 = sigma_graph(list_articles, 1996, 2019)
+G_1996_2020, df_1996_2020 = sigma_graph(list_articles, 1996, 2020)
+G_1996_2021, df_1996_2021 = sigma_graph(list_articles, 1996, 2021)
+G_1996_2022, df_1996_2022 = sigma_graph(list_articles, 1996, 2022)
+G_1996_2023, df_1996_2023 = sigma_graph(list_articles, 1996, 2023)
+
+networks = {
+'1996-2015': G_1996_2015,
+'1996-2016': G_1996_2016,
+'1996-2017': G_1996_2017,
+'1996-2018': G_1996_2018,
+'1996-2019': G_1996_2019,
+'1996-2020': G_1996_2020,
+'1996-2021': G_1996_2021,
+'1996-2022': G_1996_2022,
+'1996-2023': G_1996_2023
+}
+
+
+#max(nx.find_cliques(G_1996_2015), key=len)
+#max(nx.find_cliques(G_1996_2016), key=len)
+#max(nx.find_cliques(G_1996_2017), key=len)
+#max(nx.find_cliques(G_1996_2018), key=len)
+#max(nx.find_cliques(G_1996_2019), key=len)
+#max(nx.find_cliques(G_1996_2020), key=len)
+#max(nx.find_cliques(G_1996_2021), key=len)
+#max(nx.find_cliques(G_1996_2022), key=len)
+#max(nx.find_cliques(G_1996_2023), key=len)
+
+
+```
+
+```{=html}
+<iframe width="1300" height="900" src="networks/authors/1996_2023_VF.html" title="Sigma graph 1996-2023" frameborder=0 class="screen-inset-right"></iframe>
+```
+# Analysis of the networks
+
+Let's sort by the Burt constraint weighted and see the top 10 authors with the lowest Burt constraint weighted. You can filter by different metrics: Burt's constraint, number of citations, betweenness centrality, closeness centrality, hub score, authority score (HITS)...
+
+```{r}
+#| label: analysis-network
+#| column: screen-inset-right
+
+#create column "rank" based on citations
+df_1996_2023 <- py$df_1996_2023 %>%
+ dplyr::mutate(rank_citations =as.integer(rank(desc(citations))))
+
+reactable_burt <- df_1996_2023 %>%
+ dplyr::select(Node, rank_citations, citations, burt_constraint_weighted, betweenness, closeness, hub_score, authority_score, affilname, affiliation_country, community) %>%
+arrange(burt_constraint_weighted)
+# center headers in reactable
+reactable(reactable_burt,
+searchable =TRUE,
+defaultPageSize =5,
+highlight =TRUE,
+bordered =TRUE,
+striped =TRUE,
+compact =TRUE,
+defaultColDef =colDef(
+header =function(value) gsub("_", " ", (capitalize(value)), fixed =TRUE),
+cell =function(value) format(value, nsmall =1),
+align ="center",
+minWidth =70,
+headerStyle =list(background ="#f7f7f8")
+ ))
+
+```
+
+```{r}
+#| label: correlation-matrix-2
+
+# Select only the numeric columns from the dataframe
+df_numeric <- df_1996_2023[sapply(df_1996_2023, is.numeric)]
+
+# Calculate the correlation matrix and round the values to one decimal point
+corr <-round(cor(df_numeric, use ="complete.obs"), 2)
+
+# Calculate a matrix of p-values for the correlations
+p.mat <-cor_pmat(df_numeric)
+
+# Visualize the lower triangle of the correlation matrix using ggcorrplot
+# The heatmap color indicates the correlation coefficient between variables
+# 'hc.order' is set to TRUE to reorder the matrix using hierarchical clustering
+# 'type' is set to "lower" to show only the lower triangle of the matrix
+# 'outline.col' is set to "white" to define the border color around the squares
+corr.plot <-ggcorrplot(
+ corr, hc.order =TRUE, type ="lower", outline.col ="white",
+p.mat = p.mat
+)
+
+# Convert the ggcorrplot object to an interactive plotly object
+# This allows for interactive features such as tooltips on hover
+ggplotly(corr.plot)
+```
+
+```{r}
+#| label: correlation-matrix-3
+
+# Compute correlation coefficients
+cor.coef <-cor(df_numeric)
+
+# Compute correlation p-values
+cor.test.p <-function(x){
+ FUN <-function(x, y) cor.test(x, y)[["p.value"]]
+ z <-outer(
+colnames(x),
+colnames(x),
+Vectorize(function(i,j) FUN(x[,i], x[,j]))
+ )
+dimnames(z) <-list(colnames(x), colnames(x))
+ z
+}
+p <-cor.test.p(df_numeric)
+
+
+# Create the heatmap
+heatmaply_cor(
+ cor.coef,
+node_type ="scatter",
+point_size_mat =-log10(p),
+point_size_name ="-log10(p-value)",
+label_names =c("x", "y", "Correlation")
+)
+```
+
+## An interesting metric: the graph density
+
+The graph density is the ratio of the number of edges to the maximum number of possible edges. It is a measure of the proportion of edges present in a graph. A graph with a high density has a large number of edges compared to the number of nodes. A graph with a low density has a small number of edges compared to the number of nodes.
+
+A more formal definition is given [here](https://networkx.org/documentation/stable/reference/generated/networkx.classes.function.density.html) by the following formulas:
+
+- For undirected graphs:
+
+ $$
+ \begin{equation}d=\frac{2 m}{n(n-1)}\end{equation}
+ $$
+
+- For undirected graphs:
+
+$$
+\begin{equation}d=\frac{m}{n(n-1)}\end{equation}
+$$
+
+where $n$ is the number of nodes and $m$ is the number of edges in the graph.
+
+From an interpretation standpoint, we can appreciate the density in the graphs bellow as follows:
+
++--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| $d$ | **Interpretation** |
++:============:+:===========================================================================================================================================================+
+| Close to $0$ | - The collaborative relationships among authors are sparse: |
+| | |
+| | - Authors have limited connections with each other outside of their community. |
+| | |
+| | - Scientific papers are primarily the work of individual authors or small isolated groups. |
++--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Close to $1$ | - Authors frequently collaborate with one another, leading to a web of interconnected scientific collaborations. |
+| | |
+| | - Scientific papers often involve contributions from multiple authors, reflecting a high level of teamwork and interdisciplinary research. |
+| | |
+| | - Collaborations are a significant aspect of the research process in this marketing field, and authors actively seek out opportunities to work together. |
+| | |
+| | - The network of collaborations is well-established and robust, facilitating the exchange of ideas and the advancement of scientific knowledge. |
++--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
+
+Let's compare the density of multiple networks over time :
+
+```{python}
+#| label: density-networks
+
+# Compute Density of all the networks created above with networkx
+
+density_df = pd.DataFrame(columns=["network", "density"])
+
+temp_dfs = []
+
+for network_name, network in networks.items():
+ density = nx.density(network)
+ nb_nodes = network.number_of_nodes()
+ nb_edges = network.number_of_edges()
+ linear_density = nb_nodes / nb_edges if nb_edges !=0else0
+ nb_communities =len(set(nx.get_node_attributes(network, "community").values()))
+ nb_mean_nodes_per_community = nb_nodes / nb_communities
+ temp_df = pd.DataFrame([{
+"network": network_name,
+"density": density,
+"nb_edges": nb_edges,
+"nb_nodes": nb_nodes,
+"linear_density": linear_density,
+"nb_communities": nb_communities,
+"nb_mean_nodes_per_community": nb_mean_nodes_per_community
+ }])
+ temp_dfs.append(temp_df)
+
+# Concat all the temp_dfs into one dataframe
+density_df = pd.concat(temp_dfs, ignore_index=True)
+```
+
+```{r}
+#| label: density-networks-2
+#| fig.width: 18
+#| fig.height: 18
+#| fig-dpi: 300
+#| fig.align: center
+#| column: screen-inset-right
+
+density_df <- py$density_df
+
+density_long <- density_df %>%
+pivot_longer(
+cols =-network, # Exclude column
+names_to ="metric", # Name of new column for the metrics
+values_to ="value"# Name of new column for the values
+ ) %>%
+filter(!metric %in%c("linear_density","nb_nodes")) %>%
+mutate(value =round(value, 3))
+
+
+metric_titles <-c(density ="Density", nb_edges ="Number of Collaborations", nb_communities ="Number of Communities", nb_mean_nodes_per_community ="Mean Number of Authors per Community")
+
+# Créer le graphe avec ggplot
+density_plot <-ggplot(density_long, aes(x = network, y = value)) +
+geom_line(aes(group = metric), color ="steelblue") +
+geom_point(color ="steelblue") +
+labs(title ="",
+x ="Networks",
+y ="Value") +
+facet_wrap(~ metric, scales ="free_y", labeller =labeller(metric = metric_titles)) +
+theme_bw()+
+theme(plot.title =element_text(hjust =0.5))
+
+
+ggsave("images/density_networks.png", density_plot, width =14, height =8, dpi =300)
+
+ggplotly(density_plot)
+```
+
+## Communities
+
+### Pareto chart of citations per community
+
+```{r}
+#| label: communities
+
+# Let's look at the communities of the network 1996-2023
+
+top5_communities <- df_1996_2023 %>%
+group_by(community) %>%
+ dplyr::summarise(n =n()) %>%
+arrange(desc(n)) %>%
+head(5)
+
+
+# Compute the number of community per network
+filtered_df <- df_1996_2023 %>%
+filter(community %in% top5_communities$community)
+
+#print("The most important communities in terms of number of authors are :")
+#top5_communities %>% gt() %>% tab_header(title = "Top 5 communities in terms of number of authors") %>% cols_label(community = "Community", n = "Number of authors")
+
+```
+
+```{r}
+#| label: communities-2
+#| fig.width: 13
+#| fig.height: 6
+#| fig-dpi: 300
+#| fig.align: center
+#| column: screen-inset-right
+
+# Compute the sum of citations per community
+citations_per_community <- df_1996_2023 %>%
+group_by(community) %>%
+ dplyr::summarise(citations =sum(citations)) %>%
+arrange(-citations) %>%
+ dplyr::mutate(cumulative_citations =cumsum(citations),
+percentage = cumulative_citations /sum(citations) *100)
+
+citations_per_community %>%datatable(rownames =FALSE, extensions ='Buttons', options =list(dom ='Bfrtip', buttons =c('copy', 'csv', 'excel', 'pdf', 'print')))
+
+# No ticks and labels
+ggplot(citations_per_community, aes(x =reorder(community, -citations), y = citations)) +
+geom_bar(stat ="identity", fill ="steelblue") +
+geom_point(aes(y = cumulative_citations), group =1, color ="darkred", size =1) +
+scale_y_continuous(
+name ="Cumulative number of citations",
+sec.axis =sec_axis(~./max(citations_per_community$cumulative_citations) *100, name ="Cumulative percentage")
+ ) +
+geom_hline(yintercept =max(citations_per_community$cumulative_citations) *0.25, linetype ="dashed", color ="black") +
+geom_hline(yintercept =max(citations_per_community$cumulative_citations) *0.50, linetype ="dashed", color ="black") +
+geom_hline(yintercept =max(citations_per_community$cumulative_citations) *0.75, linetype ="dashed", color ="black") +
+theme_bw() +
+theme(
+panel.border =element_blank(),
+panel.grid.major =element_blank(),
+panel.grid.minor =element_blank(),
+axis.line =element_line(colour ="black"),
+axis.ticks.x =element_blank(), # Supprimer les marques de graduation sur l'axe des x
+axis.text.x =element_blank() # Rendre les étiquettes de l'axe des x invisibles
+ ) +
+labs(
+title ="Pareto chart of citations per community",
+x ="Community",
+y ="Number of Citations"
+ )
+
+
+ggsave("images/pareto_chart_ggplot_no_xtickslabels.png", width =20, height =10, dpi =300)
+
+```
+
+### Proportion of the top 5 communities in terms of citations and authors
+
+```{r}
+#| label: gt
+#| column: screen-inset-right
+# Sum of all citations from filtered_df for each community of the top 5
+top5_communities_citations <- filtered_df %>%
+group_by(community) %>%
+ dplyr::summarise(citations =sum(citations)) %>%
+arrange(desc(citations))
+
+sum_citations_df_1996_2023 <-sum(df_1996_2023$citations)
+
+# Compute the proportion of citations and number of authors for each community
+top5_communities_citations <- top5_communities_citations %>%
+left_join(top5_communities, by ="community") %>%
+ dplyr::mutate(prop_citations = citations / sum_citations_df_1996_2023) %>%
+ dplyr::mutate(prop_authors = n /nrow(df_1996_2023)) %>%
+ dplyr::mutate(cumul_prop_citations =round(cumsum(prop_citations) *100,2)) %>%
+ dplyr::mutate(cumul_prop_authors =round(cumsum(prop_authors) *100,2)) %>%
+ dplyr::mutate(prop_citations =round(prop_citations *100,2)) %>%
+ dplyr::mutate(prop_authors =round(prop_authors *100,2)) %>%
+arrange(desc(citations))
+
+gt_top5_communities_citations <- top5_communities_citations %>%
+gt() %>%
+tab_header(title ="Proportion of citations and authors for each community") %>%
+cols_label(community ="Community",
+citations ="Number of citations",
+n ="Number of authors",
+prop_citations ="Proportion of citations (in %)",
+prop_authors ="Proportion of authors (in %)",
+cumul_prop_citations ="Cumulative proportion of citations (in %)",
+cumul_prop_authors ="Cumulative proportion of authors (in %)") %>%
+cols_align(align ="center", columns =everything())
+
+# save gt in png
+#gtsave(gt_top5_communities_citations, "images/top5_communities_citations.png")
+#gtsave(gt_top5_communities_citations, filename = "top5_communities_citations.png")
+
+
+gt_top5_communities_citations
+#pareto.chart(citations_per_community$citations, ylab = "Cumulative proportion of citations", col = "steelblue", main = "Pareto chart of the citations per community")
+
+```
+
+```{r}
+#| label: regression
+#| eval: false
+#| echo: false
+
+#model <- lm(citations ~ inverse_burt_weighted + closeness + community + color, data = df_0_2023)
+#summary(model)
+#plot(density(df_0_2023$citations), main = "Density Plot of Citations", xlab = "Citations")
+#hist(df_0_2023$citations, main = "Distribution of Citations", xlab = "Citations")
+
+#shapiro.test(residuals(model))
+
+#plot(model, which = 1)
+
+#stargazer::stargazer(model, type = "text")
+
+#library(MASS)
+
+#model_negbin <- glm.nb(citations ~ inverse_burt_weighted + closeness + community + color, data = df_0_2023)
+
+#summary(model_negbin)
+#stargazer::stargazer(model_negbin, type = "text")
+
+```
+
+```{r}
+#| label: density-citations
+#| fig.width: 10
+#| fig.height: 6
+#| fig.align: center
+#| eval: false
+#| echo: false
+
+df_0_2023 %>%
+ggplot(aes(x =log(citations +1),
+y = ..density..)) +
+geom_histogram(bins =10, fill ="lightblue", color ="black") +
+geom_density(color ="black",
+size =1)+
+theme_bw() +
+labs(title ="Distribution of citations (log scale)", x ="Citations", y ="Frequency")+
+theme(plot.title =element_text(hjust =0.5))+
+theme(axis.text.x =element_text(size =12),
+axis.text.y =element_text(size =12),
+axis.title.x =element_text(size =14),
+axis.title.y =element_text(size =14),
+plot.title =element_text(size =16, face ="bold"))
+
+
+
+ggplot(df_0_2023, aes(x = citations,
+y = ..density..)) +
+geom_histogram(binwidth =10, fill ="lightblue", color ="black") +
+geom_density(color ="black",
+size =0.5)+
+labs(title ="Distribution of citations", x ="Citations", y ="Frequency") +
+theme_bw()
+```
diff --git a/SLR_stm.qmd b/SLR_stm.qmd
index 54e586c..f4cf26d 100644
--- a/SLR_stm.qmd
+++ b/SLR_stm.qmd
@@ -1,5 +1,6 @@
---
title: "Systematic Literature Review"
+subtitle: "Diffusion of NLP methods in the field of marketing"
date: "today"
date-format: long
author:
@@ -48,7 +49,6 @@ bibliography: slrbib.bib
```{r}
#| label : libraries-data-r
-knitr::opts_chunk$set(echo = TRUE,message=FALSE, warning=FALSE)
library(ape)
library(cowplot)
library(DT)
@@ -61,6 +61,7 @@ library(gt)
library(heatmaply)
library(Hmisc)
library(plotly)
+library(plyr)
library(quanteda)
library(quanteda.textmodels)
library(quanteda.textstats)
@@ -69,13 +70,15 @@ library(reactable)
library(reticulate)
library(Rtsne)
library(splitstackshape)
-library(tidyr)
+library(stm)
+library(tidyverse)
library(webshot)
+library(wesanderson)
library(word2vec)
+
theme_set(theme_minimal())
-library(tidyverse)
df <- read_delim("nlp_full_data_final_18-08-2023.csv",delim = ";",
escape_double = FALSE, trim_ws = TRUE) %>%
select(1,5,7,8, 14, 15,16,21,23,32,33,39,40,51,52,53) %>%
@@ -97,11 +100,33 @@ list_articles$review2<- ifelse(str_detect(list_articles$review2, "Conference")==
list_articles$review2<- ifelse(str_detect(list_articles$review2, "Transactions")==TRUE, "out", list_articles$review2)
list_articles <- list_articles %>% filter(review2 !="out")
-```
+test <- read_csv2("nlp_full_data_05-12-2023.csv") %>%
+ relocate(entry_number) %>%
+ rename(id = "entry_number") %>%
+ select(id, id, authname, surname, affilname, affiliation_city, affiliation_country, title, review, text, citedby_count, author_count, keywords, year, subjects_area, nref) %>%
+ group_by(entry_number) %>%
+ filter(row_number()==1) %>%
+ rename(id=1, title =8, review=9, text=10, keywords =13)
+test <- read_delim("nlp_full_data_05-12-2023.csv",delim = ";",
+ escape_double = FALSE, trim_ws = TRUE) %>%
+ relocate(entry_number) %>%
+ select(1,5,7,8, 14, 15,16,21,23,32,33,39,40,51,52,53) %>%
+ group_by(entry_number) %>%
+ filter(row_number()==1) %>%
+ rename(id=1,title =8, review=9, text=10, keywords =13)
+
+test <- read_csv2("nlp_full_data_05-12-2023.csv")%>%
+ rename(id = "entry_number", title = `dc:title`, authname = `dc:creator`, review = `prism:publicationName`, text = `dc:description`, keywords = "authkeywords", year = `prism:coverDate`)
+
+intersect(colnames(df), colnames(test))
+colnames(df)
+
+```
+
```{python}
-#| label : libraries-data-python
+#| label: libraries-data-python
import community as community_louvain
import pandas as pd
@@ -123,10 +148,10 @@ list_articles = r.list_articles
# Description
```{r}
-#| label : description
-#| fig-height: 7
-#| fig-width: 17
-#| column: page
+#| label: description
+#| fig-height: 6
+#| fig-width: 14
+#| column: screen-inset-right
df<- df%>%filter(review2 !="out")
@@ -163,17 +188,15 @@ plotgrid <- plot_grid(g01, g02, labels = c('A', 'B'), label_size = 8, ncol=2, r
ggsave(filename="./images/quant.jpeg", plotgrid, width = 40, height = 18, units = "cm")
-
```
# Keywords
```{r}
-#| label : keywords
+#| label: keywords1
#| fig-height: 5
-#| fig-width: 10
-#| column: page
-#| fig-align: center
+#| fig-width: 13
+#| column: screen-inset-right
user2<- df %>%
select(id, keywords)%>%
@@ -206,7 +229,6 @@ user2$keywords[user2$keywords=="reviews"]<-"online reviews"
user2$keywords[user2$keywords=="topic modelling"]<-"topic modeling"
user2$keywords[user2$keywords=="user generated content"]<-"user-generated content"
-
# compter les mots cles
foo<- user2 %>%
mutate(n=1) %>%
@@ -226,7 +248,7 @@ ggplot(foo1, aes(label = keywords, size = m)) +
ggsave(filename="./images/keywords01.jpeg", plot=last_plot(), width = 27, height = 18, units = "cm")
-#projection tsne
+# tsne projection
foo2<-user2 %>%
select(-Rang)%>%
@@ -243,14 +265,15 @@ foo3<-t(foo2)
tsne_out <- Rtsne(foo3,perplexity = 10, dim=2, check_duplicates = FALSE) # Run TSNE
tsne_out2<-as.data.frame(tsne_out$Y)
keywords<-rownames(foo3)
-library(ggrepel)
+
tsne_out3<-cbind(tsne_out2, keywords)
tsne_out3<- merge(tsne_out3,foo1)
tsneplot <- tsne_out3%>%
filter(m>0) %>%
ggplot(aes(x=V1, y=V2, label=keywords))+
geom_point(aes(size=m), alpha=.5)+
- geom_text_repel(aes(label=ifelse(m>1,keywords,""),size=log(m)), max.overlaps =50)
+ geom_text_repel(aes(label=ifelse(m>1,keywords,""),size=log(m)), max.overlaps =50)+
+ scale_size_continuous(range = c(2, 10))
tsneplot
ggsave(filename="./images/keywords.jpeg", tsneplot, width = 27, height = 18, units = "cm")
@@ -261,11 +284,10 @@ ggsave(filename="./images/keywords.jpeg", tsneplot, width = 27, height = 18, uni
The strategy is to reduce the morphologies of the most frequent keywords, in the form of unigrams, and then re-decompose the terms that make up the non-recoded keywords.
```{r}
-#| label : keywords2
+#| label: keywords2
#| fig-height: 5
-#| fig-width: 10
-#| column: page
-#| fig-align: center
+#| fig-width: 15
+#| column: screen-inset-right
key1<- df %>%
select(id, keywords)%>%
@@ -302,7 +324,7 @@ key1<- df %>%
keywords=str_replace(keywords, "electronic word of mouth (ewom)", "ewom"),
keywords=str_replace(keywords, "online word-of-mouth", "ewom"),
keywords=str_replace(keywords, "online word of mouth", "ewom"),
- keywords=str_replace(keywords, "online word of mouth", "ewom"),
+ keywords=str_replace(keywords, "online word of mouth", "ewom"),
keywords=str_replace(keywords, "negative word-of-mouth nwom", "ewom"),
keywords=str_replace(keywords, "word-of-mouth communication", "ewom"),
keywords=str_replace(keywords, "word-of-mouth", "ewom"),
@@ -354,19 +376,28 @@ foo3<-t(foo2)
tsne_out <- Rtsne(foo3,perplexity = 25, dim=2, check_duplicates = FALSE) # Run TSNE
tsne_out2<-as.data.frame(tsne_out$Y)
keywords<-rownames(foo3)
-library(ggrepel)
+
tsne_out3<-cbind(tsne_out2, keywords)
tsne_out3<- merge(tsne_out3,key3)
tsne_out3%>%
ggplot(aes(x=V1, y=V2, label=keywords))+
- geom_text_repel(aes(label=ifelse(k>4,keywords,""),size=k), max.overlaps =50)
+ geom_text_repel(aes(label=ifelse(k>4,keywords,""),size=k), max.overlaps =50)+
+ scale_size_continuous(range = c(1, 10))
ggsave(filename="./images/keywords03.jpeg", plot=last_plot(), width = 27, height = 18, units = "cm")
+```
+
+```{r}
+#| label: keywords3
+#| fig-height: 3
+#| fig-width: 10
+#| column: screen-inset-right
+
foo<- df%>%select(id,year)
foo5<-key2 %>%
@@ -379,7 +410,18 @@ foo5<-key2 %>%
filter(keywords=="ai"| keywords=="textmining"| keywords=="nlp" | keywords=="topics" |
keywords=="lda"| keywords=="ml"|keywords=="embbeding" |keywords=="liwc")
-ggplot(foo5, aes(x=year, y=m, group=keywords))+geom_area(aes(fill=keywords))
+ggplot(foo5, aes(x=year, y=m, group=keywords))+
+ geom_area(aes(fill=keywords))+
+ scale_fill_manual(values = rev(wes_palette("Zissou1", n = length(unique(foo5$keywords)), type = "continuous")))+ #nice palette
+ scale_x_continuous(breaks = unique(foo5$year)) #show every year on x axis
+
+```
+
+```{r}
+#| label: keywords4
+#| fig-height: 3
+#| fig-width: 10
+#| column: screen-inset-right
foo5<-key2 %>%
group_by(id,keywords)%>%
@@ -390,10 +432,13 @@ foo5<-key2 %>%
left_join(key3)%>%
filter(keywords=="ewom"| keywords=="reviews"| keywords=="socialmedia" | keywords=="ugc"| keywords=="bibliometric")
-ggplot(foo5, aes(x=year, y=m, group=keywords))+geom_area(aes(fill=keywords))
-
+ggplot(foo5, aes(x=year, y=m, group=keywords))+
+ geom_area(aes(fill=keywords))+
+ scale_fill_manual(values = rev(wes_palette("Zissou1", n = length(unique(foo5$keywords)), type = "continuous")))+
+ scale_x_continuous(breaks = unique(foo5$year)) #show every year on x axis
```
+
# Structural topic model (STM)
## Data preparation
@@ -401,8 +446,7 @@ ggplot(foo5, aes(x=year, y=m, group=keywords))+geom_area(aes(fill=keywords))
```{r}
#| label: data-preparation
-library(quanteda)
-library(stm)
+
df$Texte<-paste(df$title, " . ", df$text, df$keywords)
df$year<-ifelse(df$year<2011,2010,
@@ -492,9 +536,13 @@ labelTopics(stm_model, topics = NULL, n = 5, frexweight = 0.5)
$FREX = \left(\frac{w}{F} + \frac{1-w}{E}\right)^{-1}$
-see lift: $Lift = \beta_{k,v}/(w_v/\sum_v w_v)$
+see lift:
+
+$Lift = \beta_{k,v}/(w_v/\sum_v w_v)$
-the score: $\beta_{v, k} (\log \beta_{w,k} - 1 / K \sum_{k'} \log \beta_{v,k'})$
+the score:
+
+$\beta_{v, k} (\log \beta_{w,k} - 1 / K \sum_{k'} \log \beta_{v,k'})$
```{r}
#| label: topics-description
@@ -582,17 +630,15 @@ ggsave(paste0("./images/stm_topic",K,".jpg"),
#| fig-height: 5
#| fig-width: 10
#| fig-align: center
+
foo<-stm_effects$parameters
-library(tidyr)
-library(plyr)
#pour separer les liste et les transformer en df
#attention ça pertube
foo1<- ldply(foo, data.frame)%>%
select(contains('est'))
-library(dplyr)
F<-data.frame(param=c("cte", "2015", "2019","2020","2021","2022","2023"))
@@ -681,15 +727,15 @@ ggsave(paste0("./images/stm_effect",K,".jpg"),
```{r}
#| label: entropy
-#| fig-height: 5
-#| fig-width: 8
-#| column: page
+#| fig-height: 8
+#| fig-width: 10
#| fig-align: center
+
nb.cols <- 16
mycolors <- colorRampPalette(brewer.pal(8, "Set2"))(nb.cols)
-ggplot(foo4, aes(x=param,y=mean2,group=topic))+
+topics_plot <- ggplot(foo4, aes(x=param,y=mean2,group=topic))+
geom_bar(stat="identity", aes(fill=topic))+
# geom_smooth(se = FALSE )+
theme(axis.text.y = element_text(size=5))+
@@ -697,6 +743,8 @@ ggplot(foo4, aes(x=param,y=mean2,group=topic))+
labs(x=NULL, y="prévalence")+
scale_fill_manual(values = mycolors)
+ggplotly(topics_plot)
+
#entropie
@@ -709,6 +757,7 @@ foo5$param<-as.numeric(foo5$param)
ggplot(foo5,aes(x=param, y=entropie, group = 1))+
geom_point(stat = "identity")+geom_smooth(se=FALSE)+ylim(1.5,3)
+
```
# Networks of authors
@@ -990,40 +1039,41 @@ networks = {
}
-max(nx.find_cliques(G_1996_2015), key=len)
-max(nx.find_cliques(G_1996_2016), key=len)
-max(nx.find_cliques(G_1996_2017), key=len)
-max(nx.find_cliques(G_1996_2018), key=len)
-max(nx.find_cliques(G_1996_2019), key=len)
-max(nx.find_cliques(G_1996_2020), key=len)
-max(nx.find_cliques(G_1996_2021), key=len)
-max(nx.find_cliques(G_1996_2022), key=len)
-max(nx.find_cliques(G_1996_2023), key=len)
+#max(nx.find_cliques(G_1996_2015), key=len)
+#max(nx.find_cliques(G_1996_2016), key=len)
+#max(nx.find_cliques(G_1996_2017), key=len)
+#max(nx.find_cliques(G_1996_2018), key=len)
+#max(nx.find_cliques(G_1996_2019), key=len)
+#max(nx.find_cliques(G_1996_2020), key=len)
+#max(nx.find_cliques(G_1996_2021), key=len)
+#max(nx.find_cliques(G_1996_2022), key=len)
+#max(nx.find_cliques(G_1996_2023), key=len)
```
```{=html}
-
+
```
# Analysis of the networks
-Let's sort by the Burt constraint weighted and see the top 10 authors with the lowest Burt constraint weighted.
-You can filter by different metrics: Burt's constraint, number of citations, betweenness centrality, closeness centrality, hub score, authority score (HITS)...
+Let's sort by the Burt constraint weighted and see the top 10 authors with the lowest Burt constraint weighted. You can filter by different metrics: Burt's constraint, number of citations, betweenness centrality, closeness centrality, hub score, authority score (HITS)...
```{r}
#| label: analysis-network
#| column: screen-inset-right
-df_1996_2023 <- py$df_1996_2023
+#create column "rank" based on citations
+df_1996_2023 <- py$df_1996_2023 %>%
+ dplyr::mutate(rank_citations = as.integer(rank(desc(citations))))
reactable_burt <- df_1996_2023 %>%
- dplyr::select(Node, citations, burt_constraint_weighted, betweenness, closeness, hub_score, authority_score, affilname, affiliation_country, community) %>%
+ dplyr::select(Node, rank_citations, citations, burt_constraint_weighted, betweenness, closeness, hub_score, authority_score, affilname, affiliation_country, community) %>%
arrange(burt_constraint_weighted)
# center headers in reactable
reactable(reactable_burt,
searchable = TRUE,
- defaultPageSize = 10,
+ defaultPageSize = 5,
highlight = TRUE,
bordered = TRUE,
striped = TRUE,
@@ -1038,7 +1088,6 @@ reactable(reactable_burt,
```
-
```{r}
#| label: correlation-matrix-2
@@ -1049,8 +1098,6 @@ df_numeric <- df_1996_2023[sapply(df_1996_2023, is.numeric)]
corr <- round(cor(df_numeric, use = "complete.obs"), 2)
# Calculate a matrix of p-values for the correlations
-# Note: 'cor_pmat' is typically a custom function or part of a specific package.
-# Make sure you have defined or installed any package that includes this function.
p.mat <- cor_pmat(df_numeric)
# Visualize the lower triangle of the correlation matrix using ggcorrplot
@@ -1106,10 +1153,11 @@ A more formal definition is given [here](https://networkx.org/documentation/stab
- For undirected graphs:
-$$
-\begin{equation}d=\frac{2 m}{n(n-1)}\end{equation}
-$$
-- For directed graphs:
+ $$
+ \begin{equation}d=\frac{2 m}{n(n-1)}\end{equation}
+ $$
+
+- For undirected graphs:
$$
\begin{equation}d=\frac{m}{n(n-1)}\end{equation}
@@ -1120,7 +1168,7 @@ where $n$ is the number of nodes and $m$ is the number of edges in the graph.
From an interpretation standpoint, we can appreciate the density in the graphs bellow as follows:
+--------------+------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| $d$ | **Interpretation** |
+| $d$ | **Interpretation** |
+:============:+:===========================================================================================================================================================+
| Close to $0$ | - The collaborative relationships among authors are sparse: |
| | |
@@ -1172,11 +1220,11 @@ density_df = pd.concat(temp_dfs, ignore_index=True)
```{r}
#| label: density-networks-2
-#| column: screen-inset-right
-#| fig.width: 15
-#| fig.height: 20
+#| fig.width: 18
+#| fig.height: 18
#| fig-dpi: 300
#| fig.align: center
+#| column: screen-inset-right
density_df <- py$density_df
@@ -1192,7 +1240,7 @@ density_long <- density_df %>%
metric_titles <- c(density = "Density", nb_edges = "Number of Collaborations", nb_communities = "Number of Communities", nb_mean_nodes_per_community = "Mean Number of Authors per Community")
-# Créer le graphe avec ggplot
+# Creat ggplot graph to transform into plotly
density_plot <- ggplot(density_long, aes(x = network, y = value)) +
geom_line(aes(group = metric), color = "steelblue") +
geom_point(color = "steelblue") +
@@ -1200,8 +1248,8 @@ density_plot <- ggplot(density_long, aes(x = network, y = value)) +
x = "Networks",
y = "Value") +
facet_wrap(~ metric, scales = "free_y", labeller = labeller(metric = metric_titles)) +
- theme(plot.title = element_text(hjust = 0.5)) +
- theme_bw()
+ theme_bw()+
+ theme(plot.title = element_text(hjust = 0.5))
ggsave("images/density_networks.png", density_plot, width = 14, height = 8, dpi = 300)
@@ -1215,9 +1263,6 @@ ggplotly(density_plot)
```{r}
#| label: communities
-#| fig.width: 16
-#| fig.height: 10
-#| fig-dpi: 300
# Let's look at the communities of the network 1996-2023
@@ -1233,14 +1278,14 @@ filtered_df <- df_1996_2023 %>%
filter(community %in% top5_communities$community)
#print("The most important communities in terms of number of authors are :")
-top5_communities %>% gt() %>% tab_header(title = "Top 5 communities in terms of number of authors") %>% cols_label(community = "Community", n = "Number of authors")
+#top5_communities %>% gt() %>% tab_header(title = "Top 5 communities in terms of number of authors") %>% cols_label(community = "Community", n = "Number of authors")
```
```{r}
#| label: communities-2
-#| fig.width: 16
-#| fig.height: 10
+#| fig.width: 13
+#| fig.height: 6
#| fig-dpi: 300
#| fig.align: center
#| column: screen-inset-right
@@ -1263,6 +1308,7 @@ ggplot(citations_per_community, aes(x = reorder(community, -citations), y = cita
name = "Cumulative number of citations",
sec.axis = sec_axis(~./max(citations_per_community$cumulative_citations) * 100, name = "Cumulative percentage")
) +
+ scale_x_discrete(guide = guide_axis(n.dodge=3)) +
geom_hline(yintercept = max(citations_per_community$cumulative_citations) * 0.25, linetype = "dashed", color = "black") +
geom_hline(yintercept = max(citations_per_community$cumulative_citations) * 0.50, linetype = "dashed", color = "black") +
geom_hline(yintercept = max(citations_per_community$cumulative_citations) * 0.75, linetype = "dashed", color = "black") +
@@ -1272,8 +1318,7 @@ ggplot(citations_per_community, aes(x = reorder(community, -citations), y = cita
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
- axis.ticks.x = element_blank(), # Supprimer les marques de graduation sur l'axe des x
- axis.text.x = element_blank() # Rendre les étiquettes de l'axe des x invisibles
+ axis.text.x = element_text(angle = 45, vjust = 1, hjust=1, size = 8) # Rotate x axis labels, adjust justification, and set font size
) +
labs(
title = "Pareto chart of citations per community",
@@ -1281,6 +1326,7 @@ ggplot(citations_per_community, aes(x = reorder(community, -citations), y = cita
y = "Number of Citations"
)
+#ggsave("images/pareto_chart_ggplot_xtickslabels.png", width = 20, height = 10, dpi = 300)
ggsave("images/pareto_chart_ggplot_no_xtickslabels.png", width = 20, height = 10, dpi = 300)
```
@@ -1288,7 +1334,7 @@ ggsave("images/pareto_chart_ggplot_no_xtickslabels.png", width = 20, height = 10
### Proportion of the top 5 communities in terms of citations and authors
```{r}
-#|label: gt
+#| label: gt-top5-communities
# Sum of all citations from filtered_df for each community of the top 5
top5_communities_citations <- filtered_df %>%
group_by(community) %>%
@@ -1326,13 +1372,16 @@ gt_top5_communities_citations <- top5_communities_citations %>%
gt_top5_communities_citations
-#pareto.chart(citations_per_community$citations, ylab = "Cumulative proportion of citations", col = "steelblue", main = "Pareto chart of the citations per community")
+
```
```{r}
#| label: regression
+#| eval: false
+#| echo: false
+#pareto.chart(citations_per_community$citations, ylab = "Cumulative proportion of citations", col = "steelblue", main = "Pareto chart of the citations per community")
#model <- lm(citations ~ inverse_burt_weighted + closeness + community + color, data = df_0_2023)
#summary(model)
#plot(density(df_0_2023$citations), main = "Density Plot of Citations", xlab = "Citations")
@@ -1386,4 +1435,3 @@ ggplot(df_0_2023, aes(x = citations,
labs(title = "Distribution of citations", x = "Citations", y = "Frequency") +
theme_bw()
```
-
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