Sentiment Analysis using supervised classification algorithms
Yassine Al-Amrani, Mohamed Lazaar, Kamal Eddine El-Kadiri · 2017
The exploitation of social media (forums, blogs and social networks) has become crucial due to the explosive growth of textual data from these new sources of information. Our work focuses on the Sentiment analysis resulting from the messages (SMS, Facebook, Twitter...) using original techniques of search of texts. These messages can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. This paper presents a comparison of five supervised classification algorithms: PART, Support Vector Machine, Decision Tree, Naive Bayes, and Logistic Regression.