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The Effect of the Sentence Location on Arabic Sentiment Analysis


Saud S. Alotaibi


Vol. 22  No. 5  pp. 317-319


Rich morphology language such as Arabic needs more investigation and method to improve the sentiment analysis task. Using all document parts in the process of the sentiment analysis may add some unnecessary information to the classifier. Therefore, this paper shows the ongoing work to use sentence location as a feature with Arabic sentiment analysis. Our proposed method employs a supervised sentiment classification method by enriching the feature space model with some information from the document. The experiments and evaluations that were conducted in this work show that our proposed feature in the sentiment analysis for Arabic improves the performance of the classifier compared to the baseline model.


Sentiment Classification, Negation scope, Arabic Natural Language Processing, Arabic Sentiment Sentence Classification, Machine Learning.