Multilevel sentiment analysis in arabic
This work addresses sentiment analysis for Arabic language users, but it is incremental as it applies existing methods to a specific domain without broad innovation.
The study tackled improving Arabic sentiment analysis by identifying the best machine learning method and feature vector for term and document-level classification into positive or negative categories, achieving average F-scores of 0.92 for term-level and 0.94/0.93 for document-level positive/negative classes.
In this study, we aimed to improve the performance results of Arabic sentiment analysis. This can be achieved by investigating the most successful machine learning method and the most useful feature vector to classify sentiments in both term and document levels into two (positive or negative) categories. Moreover, specification of one polarity degree for the term that has more than one is investigated. Also to handle the negations and intensifications, some rules are developed. According to the obtained results, Artificial Neural Network classifier is nominated as the best classifier in both term and document level sentiment analysis (SA) for Arabic Language. Furthermore, the average F-score achieved in the term level SA for both positive and negative testing classes is 0.92. In the document level SA, the average F-score for positive testing classes is 0.94, while for negative classes is 0.93.