Yazar "Ali, Chedi Bechikh" seçeneğine göre listele

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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.