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 . Keywords - Arabic Sentiment Analysis, Machine Learning, Convolutional Neural Networks, Word Embedding, Word2Vec for Arabic, Lexicon.
|Title of host publication||Proc. 2nd International Workshop on Arabic Script Analysis and Recognition (ASAR '18)|
|Publisher||IEEE Computer Society|
|Number of pages||6|
|Publication status||Published - 4 Oct 2018|
|Event||IEEE International Workshop on Arabic and derived Script Analysis and Recognition - London, United Kingdom|
Duration: 12 Mar 2018 → 14 Mar 2018
Conference number: 2
|Workshop||IEEE International Workshop on Arabic and derived Script Analysis and Recognition|
|Period||12/03/18 → 14/03/18|
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- Arabic Sentiment Analysis
- Machine Learning
- Convolutional Neural Networks
- Word Embedding
- Word2Vec for Arabic