The MoodWARC project aims to simplify the process of tracking public opinion over time by automating the analysis of news sentiment and visualizing trends. Manually sifting through vast numbers of news articles is labor-intensive and inefficient. This system automates the process by using web crawl datasets to extract textual content from articles within a specified date range. Utilizing Natural Language Processing (NLP) techniques, the system analyzes the sentiment of the articles, categorizing them as positive, negative, or neutral. The sentiment data is then aggregated by time period and visualized in a user-friendly time chart, enabling users to observe sentiment trends over time. Key components of the project include a data acquisition module utilizing web crawl datasets, a data preprocessing module for cleaning and preparing textual data, and a sentiment analysis module that employs NLP algorithms or machine learning models to evaluate sentiment. The data visualization module creates a time chart that displays these sentiment trends. Additionally, the project features a demo user interface, allowing users to define timeframes and view sentiment trends interactively. This interface also supports categorization by news topics. The MoodWARC thus offers an efficient, automated solution for monitoring public sentiment through news media, making trend analysis more accessible and manageable.
The scientific case for the MoodWARC project is based on several key components: 1. Natural Language Processing (NLP) Sentiment Analysis: Utilizing sentiment lexicons, machine learning models, and deep learning algorithms to categorize sentiment as positive, negative, or neutral. Text Preprocessing: Employing techniques such as tokenization, stemming, lemmatization, and noise removal (e.g., stop words, punctuation) to clean and prepare textual data. 2. Data Acquisition from Web Crawl Datasets Utilizing Web Crawl Datasets: Leveraging pre-existing web crawl datasets to collect large volumes of news articles, ensuring efficient and comprehensive data acquisition without the need to implement a web crawler. 3. Data Aggregation and Analysis Temporal Analysis: Aggregating sentiment data over specified time periods to identify trends and patterns using statistical analysis. Categorization and Filtering: Classifying articles into different categories or topics and applying user-defined criteria for targeted insights. 4. Data Visualization Trend Visualization: Creating intuitive and interactive time charts that display sentiment trends using libraries like Plotly, or similar tools. User Interface Design: Developing a user-friendly interface for users to interact with data, select timeframes, and view sentiment trends, employing UX/UI design principles for usability and accessibility. 5. Scientific Relevance and Applications Public Opinion Tracking: Providing a method for tracking public opinion on various topics over time, valuable for researchers in social sciences, political science, and communication studies. Media Analysis: Enhancing the understanding of media bias, misinformation spread, and overall media coverage tone on specific issues. Decision-Making Support: Offering data-driven insights for decision-makers in marketing, public relations, and policy-making. This combination of advanced NLP techniques, robust data acquisition from web crawl datasets, comprehensive analysis, and effective visualization makes the MoodWARC a scientifically valuable tool.
Project collaborators: amr0walid, khaledezz2001, ShadenHazem