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    Empirical Evaluation of Word Representations on Arabic Sentiment Analysis
    (SPRINGER-VERLAG BERLIN, 2018) Gridach, Mourad; Haddad, Hatem; Mulki, Hala
    Sentiment analysis is the Natural Language Processing (NLP) task that aims to classify text to different classes such as positive, negative or neutral. In this paper, we focus on sentiment analysis for Arabic language. Most of the previous works use machine learning techniques combined with hand engineering features to do Arabic sentiment analysis (ASA). More recently, Deep Neural Networks (DNNs) were widely used for this task especially for English languages. In this work, we developed a system called CNN-ASAWR where we investigate the use of Convolutional Neural Networks (CNNs) for ASA on 2 datasets: ASTD and SemEval 2017 datasets. We explore the importance of various unsupervised word representations learned from unannotated corpora. Experimental results showed that we were able to outperform the previous state-of-the-art systems on the datasets without using any kind of hand engineering features.
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    Modern Trends in Arabic Sentiment Analysis: A Survey
    (ASSOC TRADUCTION AUTOMATIQUE LINGUISTIQUE APPLIQUEE, 2017) Mulki, Hala; Haddad, Hatem; Babaoglu, Ismail
    The growth of the Arabic textual content on social media platforms has been caused by the continuous crises in the Arab World evoking the need to analyze the opinions of the public against the ongoing events. Arabic Sentiment Analysis (ASA) is, therefore, becoming the focus of many recent NLP studies. With several Arabic NLP resources being publicly available along with the emergence of deep learning techniques, researchers could handle the complex nature of Arabic language more efficiently. In the last decade, various ASA systems have been built. Yet, their achievements have not been investigated or compared against each other. This survey covers the ASA research carried out during the past five years. We compare and evaluate the performances and give insight into the ability of the created Arabic resources to support the future ASA research.
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    Preprocessing Impact on Turkish Sentiment Analysis
    (IEEE, 2018) Mulki, Hala; Haddad, Hatem; Ali, Chedi Bechikh; Babaoglu, Ismail
    This paper investigates the impact of preprocessing on sentiment classification of Turkish movies and products reviews. Input datasets were subjected to several combinations of preprocessing techniques. Later, the manipulated reviews were fed to a lexicon-based and a supervised machine learning sentiment classifiers. The achieved results emphasize the positive impact of preprocessing phase on the accuracy of both sentiment classifiers as the baseline was outperformed with a considerable margin especially when stemming, emoji recognition and negation detection tasks were applied.
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    Tunisian Dialect Sentiment Analysis: A Natural Language Processing-based Approach
    (IPN, CENTRO INVESTIGAVION COMPUTACION, 2018) Mulki, Hala; Haddad, Hatem; Ali, Chedi Bechikh; Babaoglu, Ismail
    Social media platforms have been witnessing a significant increase in posts written in the Tunisian dialect since the uprising in Tunisia at the end of 2010. Most of the posted tweets or comments reflect the impressions of the Tunisian public towards social, economical and political major events. These opinions have been tracked, analyzed and evaluated through sentiment analysis systems. In the current study, we investigate the impact of several preprocessing techniques on sentiment analysis using two sentiment classification models: Supervised and lexicon-based. These models were trained on three Tunisian datasets of different sizes and multiple domains. Our results emphasize the positive impact of preprocessing phase on the evaluation measures of both sentiment classifiers as the baseline was significantly outperformed when stemming, emoji recognition and negation detection tasks were applied. Moreover, integrating named entities with these tasks enhanced the lexicon-based classification performance in all datasets and that of the supervised model in medium and small sized datasets.