Yazar "Mulki, Hala" seçeneğine göre listele

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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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    Sentiment classification of arabic tweets using a novel learning sentiment-specific word embedding technique
    (Selçuk Üniversitesi Fen Bilimleri Enstitüsü, 2019) Mulki, Hala; Babaoğlu, İsmail
    The intensive use of social media during the "Arab Spring" incidents, has led to a sudden growth of the online Arabic opinionated content. Sentiment Analysis can recognize the opinions embedded in shared texts, providing real-time and long-term insights.With the Arabic social media data being dominated by dialectal Arabic, Arabic sentiment analysis models need to handle the complex morphological nature of the Arabic language, let alone, the non-standard grammatical properties and the variances among the Arabic dialects. Existing Arabic sentiment analysis models represent the sentiment embedded in dialectal Arabic either by hand-crafted features or text embedding ones. Hand-crafted features are usually generated based on dialect-specific Natural Language processing (NLP) tools and resources. On the other hand, text embedding features tend to use ordered, syntax-aware composition functions to produce sentence/paragraph embeddings learned within deep neural architectures. Given the current hand-crafted/embedding features, an Arabic sentiment analysis system developed for one dialect might not be efficient for the others, especially with the free word order, the varying syntactic nature and the drastic syntactic/semantic differences among the Arabic dialects. In this thesis, two dialect-independent Arabic sentiment analysis models equipped with hand-crafted and text embedding features are presented. While each model has its own type of sentiment features and classification methods, they both perform sentiment analysis of multiple Arabic dialects with the least dependence on Arabic NLP tools and without the need for external knowledge resources. In the Hand-Crafted based Tw-StAR model (HCB Tw-StAR), novel hand-crafted features based on the universal text components Named Entities (NEs) and various combinations of preprocessing tasks are proposed. Provided with these features, HCB Tw-StAR could achieve an improved sentiment classification performance for Arabic/non-Arabic contents at different analysis levels. In the second model Embedding Features-based Neural Tw-StAR (Neu Tw-StAR), novel sentiment-specific, syntaxignorant n-gram embedding features learned from labeled data and composed using the additive unordered composition function SOWE, are presented. Neu Tw-StAR trained with the proposed n-gram embeddings proved its efficiency to handle multiple Eastern and Western Arabic dialects, as it outperformed two state-of-the-art syntax-aware embedding methods: word2vec and doc2vec. Moreover, being implemented as a shallow feed-forward neural model, Neu Tw-StAR exhibited a competent and some times better performance, in addition it could decrease the consumed training time compared to deep neural models: Convolutional Neural Networks (CNN) and Long short Term Memory netwotks (LSTM) models.
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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.