Gilbert Badaro


EMA at SemEval-2018 Task 1: Emotion Mining for Arabic
Gilbert Badaro | Obeida El Jundi | Alaa Khaddaj | Alaa Maarouf | Raslan Kain | Hazem Hajj | Wassim El-Hajj
Proceedings of the 12th International Workshop on Semantic Evaluation

While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic. In fact, businesses are interested in learning a fine-grained representation of how users are feeling towards their products or services. In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets. EMA participated in all 5 subtasks. For the five tasks, several preprocessing steps were evaluated and eventually the best system included diacritics removal, elongation adjustment, replacement of emojis by the corresponding Arabic word, character normalization and light stemming. Moreover, several features were evaluated along with different classification and regression techniques. For the 5 subtasks, word embeddings feature turned out to perform best along with Ensemble technique. EMA achieved the 1st place in subtask 5, and 3rd place in subtasks 1 and 3.

EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet
Gilbert Badaro | Hussein Jundi | Hazem Hajj | Wassim El-Hajj
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

Nowadays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others. On the other hand, politicians, companies, and businesses are interested in analyzing automatically people’s opinions and emotions. In the last decade, a lot of efforts has been put into extracting sentiment polarity from texts. Recently, the focus has expanded to also cover emotion recognition from texts. In this work, we expand an existing emotion lexicon, DepecheMood, by leveraging semantic knowledge from English WordNet (EWN). We create an expanded lexicon, EmoWordNet, consisting of 67K terms aligned with EWN, almost 1.8 times the size of DepecheMood. We also evaluate EmoWordNet in an emotion recognition task using SemEval 2007 news headlines dataset and we achieve an improvement compared to the use of DepecheMood. EmoWordNet is publicly available to speed up research in the field on


A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models
Ramy Baly | Gilbert Badaro | Georges El-Khoury | Rawan Moukalled | Rita Aoun | Hazem Hajj | Wassim El-Hajj | Nizar Habash | Khaled Shaban
Proceedings of the Third Arabic Natural Language Processing Workshop

Opinion mining in Arabic is a challenging task given the rich morphology of the language. The task becomes more challenging when it is applied to Twitter data, which contains additional sources of noise, such as the use of unstandardized dialectal variations, the nonconformation to grammatical rules, the use of Arabizi and code-switching, and the use of non-text objects such as images and URLs to express opinion. In this paper, we perform an analytical study to observe how such linguistic phenomena vary across different Arab regions. This study of Arabic Twitter characterization aims at providing better understanding of Arabic Tweets, and fostering advanced research on the topic. Furthermore, we explore the performance of the two schools of machine learning on Arabic Twitter, namely the feature engineering approach and the deep learning approach. We consider models that have achieved state-of-the-art performance for opinion mining in English. Results highlight the advantages of using deep learning-based models, and confirm the importance of using morphological abstractions to address Arabic’s complex morphology.

OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model
Ramy Baly | Gilbert Badaro | Ali Hamdi | Rawan Moukalled | Rita Aoun | Georges El-Khoury | Ahmad Al Sallab | Hazem Hajj | Nizar Habash | Khaled Shaban | Wassim El-Hajj
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the “OMAM” systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topic-based approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1st in subtasks C and E, and 2nd in subtask D.


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Deep Learning Models for Sentiment Analysis in Arabic
Ahmad Al Sallab | Hazem Hajj | Gilbert Badaro | Ramy Baly | Wassim El Hajj | Khaled Bashir Shaban
Proceedings of the Second Workshop on Arabic Natural Language Processing

A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets
Gilbert Badaro | Ramy Baly | Rana Akel | Linda Fayad | Jeffrey Khairallah | Hazem Hajj | Khaled Shaban | Wassim El-Hajj
Proceedings of the Second Workshop on Arabic Natural Language Processing


A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining
Gilbert Badaro | Ramy Baly | Hazem Hajj | Nizar Habash | Wassim El-Hajj
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)