This repository is related to our work on Aspect-Based Sentiment Analysis (ABSA) of Arabic laptop reviews. Please, cite the following paper if you find the resources in this project useful:

• Mahmoud Al-Ayyoub, Amal Gigieh, Areej Al-Qwaqenah, Mohammed Al-Kabi, Bashar Talafhah and Izzat Alsmadi. Aspect-Based Sentiment Analysis of Arabic Laptop Reviews. In the proceedings of the 18th International Arab Conference on Information Technology (ACIT), 2017.

Run the baseline

Prerequisite:

java -version

openjdk version "1.8.0_275"
OpenJDK Runtime Environment (build 1.8.0_275-8u275-b01-0ubuntu1~20.04-b01)
OpenJDK 64-Bit Server VM (build 25.275-b01, mixed mode)

Baseline:

unzip baseevalvalid1.zip
cd BaseEvalValid1/
cd libsvm-3.18/
make
cd ../

# Download "absa15.conf" and "Arabic_Laptop_Reviews.xml" from the github repo, and place them in the current directory (BaseEvalValid1)
# This will replace the old "absa15.conf" with the downloaded one.

# run the baseline file
./absa15.sh

Results:

** Categories Evaluation **

#predicted=121
#gold=285
#common=64
PRE=0.5289256
REC=0.22456141
F-MEASURE=0.31527093

***** Evaluate Stage 2 Output (Polarity) *****

Comparing:/home/bashar/ABSA/2015/Files/teGldAspTrg.PrdPol.xml to /home/bashar/ABSA/2015/Files/teGld.xml
Polarity Evaluation

------------------------------------------------------------------------
label\measure    |Precision        |Recall           |F-measure        |
------------------------------------------------------------------------
positive         |0.7689(163/212)  |0.8402(163/194)  |0.803            |
negative         |0.6395(55/86)    |0.5288(55/104)   |0.5789           |
neutral          |NaN(0/0)         |NaN(0/0)         |NaN              |
conflict         |NaN(0/0)         |NaN(0/0)         |NaN              |
------------------------------------------------------------------------
Accuracy:	0.7315436 (218/298)

*******************************************

You may find the following papers from our group relevant:

• Mohammad Al-Smadi, Bashar Talafha, Mahmoud Al-Ayyoub and Yaser Jararweh. Using Long Short-Term Memory Deep Neural Networks for Aspect-Based Sentiment Analysis of Arabic Reviews. International Journal of Machine Learning and Cybernetics (JMLC). To appear. https://doi.org/10.1007/s13042-018-0799-4
• Mahmoud Al-Ayyoub, Abed Allah Khamaiseh, Mohammed Al-Kabi and Yaser Jararweh. A Comprehensive Survey of Arabic Sentiment Analysis. Journal of Information Processing and Management (IP&M) 56(2): 320-342, March 2019. https://doi.org/10.1016/j.ipm.2018.07.006
• Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yaser Jararweh and Omar Qawasmeh. Enhancing Aspect-Based Sentiment Analysis of Arabic Hotels’ Reviews using Morphological, Syntactic and Semantic Features. Journal of Information Processing and Management (IP&M) 56(2): 308-319, March 2019. https://doi.org/10.1016/j.ipm.2018.01.006
• Mohammad Al-Smadi, Omar Qawasmeh, Mahmoud Al-Ayyoub, Yaser Jararweh and Brij Gupta. Deep Recurrent Neural Network vs. Support Vector Machine for Aspect-Based Sentiment Analysis of Arabic Hotels’ Reviews. Journal of Computational Science 27: 386-393, July 2018. https://doi.org/10.1016/j.jocs.2017.11.006
• Mahmoud Al-Ayyoub, Aya Nuseir, Kholoud Alsmearat, Yaser Jararweh and Brij Gupta. Deep Learning for Arabic NLP: A Survey. Journal of Computational Science 26: 522-531, May 2018. https://doi.org/10.1016/j.jocs.2017.11.011
• Aya Nuseir, Mahmoud Al-Ayyoub, Mohammed Al-Kabi, Ghasan Kanaan and Riyad Al-Shalabi. Improved Hierarchical Classifiers for Multi-Way Sentiment Analysis. The International Arab Journal of Information Technology (IAJIT) 14(4A): 654-661, July 2017.
• Mahmoud Al-Ayyoub, Huda Al-Sarhan, Majd Al-So'ud, Mohammad Al-Smadi and Yaser Jararweh. Framework for Affective News Analysis of Arabic News: 2014 Gaza Attacks Case Study. Journal of Universal Computer Science (JUCS) 23(3): 327-351, March 2017. http://doi.org/10.3217/jucs-023-03-0327 https://github.com/malayyoub/ABSA-for-Affective-News-Analysis-
• Mohammad Al-Smadi, Islam Obaidat, Mahmoud Al-Ayyoub, Rami Mohawesh and Yaser Jararweh. Using Enhanced Lexicon-Based Approaches for the Determination of Aspect Categories and Their Polarities in Arabic Reviews. International Journal of Information Technology and Web Engineering (IJITWE) 11(3): 15-30, July-September 2016. https://doi.org/10.4018/IJITWE.2016070102
• Khalid Alkhatib, Abdullateef Rabab'ah, Mahmoud Al-Ayyoub and Yaser Jararweh. On the Use of Arabic Tweets to Predict Stock Market Changes in the Arab World. International Journal of Advanced Computer Science and Applications (IJACSA) 7(5): 560-566, May 2016. http://dx.doi.org/10.14569/IJACSA.2016.070574 https://github.com/amrababah/Stock-Market-Changes-Predictions https://www.researchgate.net/publication/333402849 https://www.researchgate.net/publication/333402582
• Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Huda Al-Sarhan and Yaser Jararweh. An Aspect-Based Sentiment Analysis Approach to Evaluating Arabic News Affect on Readers. Journal of Universal Computer Science (JUCS) 22(5): 630-649, May 2016. http://doi.org/10.3217/jucs-022-05-0630
• Mahmoud Al-Ayyoub, Aya Nuseir, Ghassan Kanaan and Riyad Al-Shalabi. Hierarchical Classifiers for Multi-Way Sentiment Analysis of Arabic Reviews. International Journal of Advanced Computer Science and Applications (IJACSA) 7(2): 531-539, February 2016. http://dx.doi.org/10.14569/IJACSA.2016.070269
• Mohammed Al-Kabi, Mahmoud Al-Ayyoub, Izzat M. Alsmadi and Heider A. Wahsheh. A Prototype for a Standard Arabic Sentiment Analysis Corpus. The International Arab Journal of Information Technology (IAJIT) 13(1A): 163-170, 2015. https://goo.gl/X8SmAO
• Mahmoud Al-Ayyoub, Safa Bani Essa and Izzat Alsmadi. Lexicon-Based Sentiment Analysis of Arabic Tweets. International Journal of Social Network Mining (IJSNM) 2(2): 101-114, 2015. https://doi.org/10.1504/IJSNM.2015.072280
• Nawaf Abdulla, Nizar Ahmed, Mohammed Shehab, Mahmoud Al-Ayyoub, Mohammed Al-Kabi and Saleh Al-rifai. Towards Improving the Lexicon-Based Approach for Arabic Sentiment Analysis. International Journal of Information Technology and Web Engineering (IJITWE) 9(3): 55-70, July 2014. https://doi.org/10.4018/ijitwe.2014070104
• Nawaf Abdulla, Mahmoud Al-Ayyoub and Mohammed Al-Kabi. An Extended Analytical Study of Arabic Sentiments. International Journal of Big Data Intelligence (IJBDI) 1(1/2): 103-113, 2014. http://dx.doi.org/10.1504/IJBDI.2014.063845
• Ftoon Abu Shaqra, Rehab Duwairi and Mahmoud Al-Ayyoub. Recognizing Emotion from Speech Based on Age and Gender Using Hierarchical Models. In The 10th International Conference on Ambient Systems, Networks and Technologies (ANT), 2019. https://doi.org/10.1016/j.procs.2019.04.009
• Bashar Talafha and Mahmoud Al-Ayyoub. IoH-RCNN: Pursuing the Ingredients of Happiness using Recurrent Convolutional Neural Networks. In The Workshops of the The Thirty-Third AAAI Conference on Artificial Intelligence, 2019.
• Omar Badarneh, Mahmoud Al-Ayyoub, Nouh Alhindawi, Lo'ai Tawalbeh and Yaser Jararweh. Fine-Grained Emotion Analysis of Arabic Tweets: A Multi-Target Multi-Label Approach. In the proceedings of the Twelfth IEEE International Conference on Semantic Computing (ICSC), 2018. https://doi.org/10.1109/ICSC.2018.00070 https://github.com/malayyoub/Fine-Grained-Emotion-Analysis-of-Arabic-Tweets
• Mohammad Al-Smadi, Omar Qawasmeh, Bashar Talafha, Mahmoud Al-Ayyoub, Yaser Jararweh and Elhadj Benkhelifa. An Enhanced Framework for Aspect-Based Sentiment Analysis of Hotels' Reviews: Arabic Reviews Case Study. In the proceedings of the 11th International Conference for Internet Technology and Secured Transactions (ICITST), 2016. https://doi.org/10.1109/ICITST.2016.7856675
• Aya Nuseir, Mohammed Al-Kabi, Mahmoud Al-Ayyoub, Ghasan Kanaan and Riyad Al-Shalabi. Improved Hierarchical Classifiers for Multi-Way Sentiment Analysis. In the 17th International Arab Conference on Information Technology (ACIT), 2016.
• Mohammed Al-Kabi, Areej Al-Qwaqenah, Amal Gigieh, Kholoud Alsmearat, Mahmoud Al-Ayyoub and Izzat Alsmadi. Building a Standard Dataset for Arabic Sentiment Analysis: Identifying Potential Annotation Pitfalls. In the proceedings of the 13th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA), 2016. https://doi.org/10.1109/AICCSA.2016.7945822
• Abdullateef Rabab'ah, Mahmoud Al-Ayyoub, Yaser Jararweh and Mohammed Al-Kabi. Evaluating SentiStrength for Arabic Sentiment Analysis. In the International Conference on Computer Science and Information Technology (CSIT), 2016. https://doi.org/10.1109/CSIT.2016.7549458
• Wegdan Hussien, Yahya Tashtoush, Mahmoud Al-Ayyoub and Mohammed Al-Kabi. Are Emoticons Good Enough to Train Emotion Classifiers of Arabic Tweets? In the International Conference on Computer Science and Information Technology (CSIT), 2016. https://doi.org/10.1109/CSIT.2016.7549459
• Maria Pontiki, Dimitrios Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeny Kotelnikov, Nuria Bel, Salud María Jiménez-Zafra and Gülşen Eryiğit. SemEval-2016 Task 5: Aspect Based Sentiment Analysis. In the 10th International Workshop on Semantic Evaluation (SemEval), 2016. https://doi.org/10.18653/v1/S16-1002 http://alt.qcri.org/semeval2016/task5/
• Huda Al-Sarhan, Majd Al-So'ud, Mohammad Al-Smadi and Mahmoud Al-Ayyoub. Framework for Affective News Analysis of Arabic News: 2014 Gaza Attacks Case Study. In the 7th International Conference on Information and Communication Systems (ICICS), 2016. https://doi.org/10.1109/IACS.2016.7476073
• Mohammad Al-A'abed and Mahmoud Al-Ayyoub. A Lexicon-Based Approach for Emotion Analysis of Arabic Social Media Content. In The International Computer Sciences and Informatics Conference (ICSIC), 2016.
• Mohammed Al-Kabi, Mahmoud Al-Ayyoub, Izzat Alsmadi and Heider Wahsheh. A Prototype for a Standard Arabic Sentiment Analysis Corpus. In the 16th International Arab Conference on Information Technology (ACIT), 2015.
• Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Huda Al-Sarhan and Yaser Jararweh. Using Aspect-Based Sentiment Analysis to Evaluate Arabic News Affect on Readers. In the proceedings of the 8th IEEE/ACM International Conference on Utility and Cloud Computing (UCC), 2015. https://doi.org/10.1109/UCC.2015.78
• Islam Obaidat, Rami Mohawesh, Mahmoud Al-Ayyoub, Mohammad Al-Smadi and Yaser Jararweh. Enhancing the Determination of Aspect Categories and Their Polarities in Arabic Reviews Using Lexicon-Based Approaches. In the proceedings of the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2015. https://doi.org/10.1109/AEECT.2015.7360595
• Mohammad Al-Smadi, Omar Qawasmeh, Bashar Talafha, and Muhannad Quwaider. Human annotated arabic dataset of book reviews for aspect based sentiment analysis. In the proceedings of the 3rd International Conference on Future Internet of Things and Cloud (FiCloud), 2015. https://doi.org/10.1109/FiCloud.2015.62
• Bashar Al Shboul, Mahmoud Al-Ayyoub and Yaser Jararweh. Multi-Way Sentiment Classification of Arabic Reviews. In the 6th International Conference on Information and Communication Systems (ICICS), 2015. https://doi.org/10.1109/IACS.2015.7103228
• Nawaf Abdulla, Roa’a Majdalawi, Salwa Mohammed, Mahmoud Al-Ayyoub and Mohammed Al-Kabi. Automatic Lexicon Construction for Arabic Sentiment Analysis. In the proceedings of the 2nd International Conference on Future Internet of Things and Cloud (FiCloud), 2014. https://doi.org/10.1109/FiCloud.2014.95
• Mohammed Al-Kabi, Nawaf Abdulla and Mahmoud Al-Ayyoub. An Analytical Study of Arabic Sentiments: Maktoob Case Study. In the proceedings of the 8th International Conference for Internet Technology and Secured Transactions (ICITST), 2013. https://doi.org/10.1109/ICITST.2013.6750168
• Nawaf Abdulla, Nizar Ahmed, Mohammed Shehab and Mahmoud Al-Ayyoub. Arabic Sentiment Analysis: Lexicon-based and Corpus-based. In the 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), 2013. https://doi.org/10.1109/AEECT.2013.6716448 https://archive.ics.uci.edu/ml/datasets/Twitter+Data+set+for+Arabic+Sentiment+Analysis
• Wegdan Hussien, Mahmoud Al-Ayyoub, Yahya Tashtoush and Mohammed Al-Kabi. On the Use of Emojis to Train Emotion Classifiers. arXiv preprint arXiv:1902.08906, February 2019.