Journal of Computer Science

Investigation of Naive Bayes Combined with Multilayer Perceptron for Arabic Sentiment Analysis and Opinion Mining

Mohammad Subhi Al-Batah, Shakir Mrayyen and Malek Alzaqebah

Journal of Computer Science


Sentiment analysis and opinion mining has recently become active research area, which associated with studying the people’s opinions, emotions and evaluation from a written text. The rapid growing of social networks increases the importance of sentiment analysis; in which people are always sharing their opinion about several subjects and topics over the internet. Therefore, it will be useful to classify people’s reviews, discussions and blogs to identify their opinion of specific product, movie or hotel ether is a positive or negative, to farther helps companies or other people for decision making. Sentiment analysis for English language has been well studied. Conversely, the work that has been carried out in terms of Arabic remains in its infancy; thus, more cooperation is required between research communities in order for them to offer a mature sentiment analysis system for Arabic. In this study, the Naive Bayes algorithm (NB) and Multilayer Perceptron (MLP) network are combined with hybrid system called NB-MLP for Arabic sentiment classification. Six datasets were tested; attraction, hotel, movie, product, restaurant and tweets. The datasets are then classified into positive or negative polarities of sentiment using the proposed system. The 10-fold cross validation was employed for testing the models. Over the whole set of experimental data, the results show that the proposed system can achieve high classification accuracy and has promising potential application in the Arabic sentiment analysis and opinion mining.


© 2018 Mohammad Subhi Al-Batah, Shakir Mrayyen and Malek Alzaqebah. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.