A genetic algorithm feature selection based approach for Arabic Sentiment Classification

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IEEE Computer Society
With the recently increasing interest for opinion mining from different research communities, there is an evolving body of work on Arabic Sentiment Analysis. There are few available polarity annotated datasets for this language, so most existing works use these datasets to test the best known supervised algorithms for their objectives. Naïve Bayes and SVM are the best reported algorithms in the Arabic sentiment analysis literature. The work described in this paper shows that using a genetic algorithm to select features and enhancing the quality of the training dataset improve significantly the accuracy of the learning algorithm. We use the LABR dataset of book reviews and compare our results with LABR’s authors’ results.
Sentiment analysis, Arabic language, Supervised learning, Genetic algorithm, Features selection
13th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2016, Agadir, Morocco, November 29 - December 2, 2016