Sentiment Analysis Classification for Text in Social Media: Application to Tunisian Dialect


Asma BelHadj Braiek, Zouhour Neji Ben Salem, Carthage University, Tunisia


Social networks are the most used means to express oneself freely and give one's opinion about a subject, an event, or an object. These networks present rich content that could be subject today to sentiment analysis interest in many fields such as politics, social sciences, marketing, and economics. However, social networkusers express themselves using their dialect. Thus, to help decision-makers in the analysis of users' opinions, it is necessary to proceed to the sentimental analysis of this dialect. The paper subject deals with a hybrid model combining a lexicon-based approach with a modified and adapted version of a sentiment rule-based engine named VADER. The hybrid model is tested and evaluated using the Tunisian Arabic Dialect, it showed good performance reaching 85% classification.


automatic language processing, sentiment analysis, text mining, emotional detection, social web, annotated corpus, sentiment lexicon, and sentiment engine.