Improving Sentiment Analysis in Arabic Using Word Representation
This work addresses sentiment analysis for Arabic language users, particularly in health contexts, but is incremental as it applies existing methods to a new dataset.
The paper tackled sentiment analysis in Arabic by constructing Word2Vec models from a large corpus of newspapers and applying machine learning algorithms and convolutional neural networks, achieving improved accuracy of 91%-95% on a health sentiment dataset.
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]