Improving Sentiment Analysis in Arabic Using Word Representation

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The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications. Word embedding, or word distributing approach, is a current and powerful tool to capture together the closest words from a contextual text. In this paper, we describe how we construct Word2Vec models from a large Arabic corpus obtained from ten newspapers in different Arab countries. By applying different machine learning algorithms and convolutional neural networks with different text feature selections, we report improved accuracy of sentiment classification (91%-95%) on our publicly available Arabic language health sentiment dataset [1]. Keywords - Arabic Sentiment Analysis, Machine Learning, Convolutional Neural Networks, Word Embedding, Word2Vec for Arabic, Lexicon.
Original languageEnglish
Title of host publicationProc. 2nd International Workshop on Arabic Script Analysis and Recognition (ASAR '18)
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)978-1-5386-1459-4
ISBN (Print) 978-1-5386-1460-0
Publication statusPublished - 4 Oct 2018
EventIEEE International Workshop on Arabic and derived Script Analysis and Recognition - London, United Kingdom
Duration: 12 Mar 201814 Mar 2018
Conference number: 2


WorkshopIEEE International Workshop on Arabic and derived Script Analysis and Recognition
Abbreviated titleASAR
CountryUnited Kingdom
Internet address

Bibliographical note

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  • Arabic Sentiment Analysis
  • Machine Learning
  • Convolutional Neural Networks
  • Word Embedding
  • Word2Vec for Arabic
  • Lexicon

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