Studying the effect of characteristic vector alteration on Arabic sentiment classification

Abstract

In this paper we propose a new approach to analyze sentiments for the Arabic language. To overcome the scarcity and size limitation of the required Arabic language resources for training and analysis tasks, we built new lexical resources using different approaches. We have also integrated the morphological notion by creating both stemmed and lemmatized versions of word lexicons. Thereafter, the generated resources were used in the construction of a supervised model from a set of features considering the word negation context. Similarly, we have semantically segmented the lexicon in order to reduce the model vectors size and consequently improve the execution time.

Keywords

• Sentiment analysis
• Opinion extraction
• Arabic language
• Lexicon
• Semantic segmentation
• Supervised approach