Abstract
Nowadays, social media are taking an essential part of our daily life in several domains. Especially, on opinion analysis area with decisional systems where it helps companies to improve their products from reviews posted on social media such as Facebook, Twitter, etc. In this respect, there is an interest for opinion analysis or opinion mining expressed in social media for business decision support. In this research paper, we propose a new method of opinion analysis based on machine learning that determines the polarity of users'comments shared on different social media. The latter will be integrated in the ETL (Extract, Transform and Load) process to analyze the users' opinions. The proposed method is based on the n-grams technique to construct a semi-automatic dictionary for positive and negative keywords that is used in the learning phase to establish the prediction model. In addition, we propose a new features vector specific for social media for classifying the comments as positive, negative or neutral. The evaluation results performed on the both publicly data sets Stanford Twitter Sentiment (STS) and Sanders dataset showed a high accuracy level.