Rough Set Theory for Arabic Sentiment Classification
Qasem A. Al‐Radaideh, Laila M. Twaiq · 2014
Recently, the web has been a major place where people interact and express their views and sentiments. Researchers were attracted to conduct further analysis on this rich content known as Sentiment Analysis, Sentiment Classification or Opinion Mining. Rough Set Theory is a mathematical tool that can be used for classification and analysis of uncertain, incomplete or vague information. It can be used to significantly reduce the dimensionality of the data without much loss in information content, which is achieved using the concept of Reduct. This paper focuses on investigating the use of the Rough Set theory approach for Arabic Sentiment Classification. This paper compares some approaches that have been proposed to find Reducts to classify Arabic tweeting reviews. The Rosetta toolkit is used for testing where two main Reduct approaches were applied: Johnson Reducer and Genetic-based reducer. We compared the results of the approaches using cross validation evaluation method. The results showed that Genetic reducer achieved 57% of accuracy, which outperformed Johnson Reducer. The paper concludes that the Rough Set based approach is applicable for sentiment analysis of Arabic text but further investigation is required to evaluate other Reduct generation methods.