A Sentiment Analyzer for Informal Text in Social Media - Le, Huong Thanh; Tran, Nhan Trong (Old Journal, 2018)
This paper introduces an approach to Twitter sentiment analysis, with the task of classifying tweets as positive, negative or neutral. In the preprocessing task, we propose a method to deal with two problems: (i) repeated characters in informal expression of words; and (ii) the affect of contrast word in determining sentence polarity. We propose features used in this task, investigate and select an optimal classifying algorithm among Decision Tree, K Nearest Neighbor, Support Vector Machine, and a Voting Classifier for solving Twitter sentiment analysis problem. Experiment results with Twitter 2016 test dataset shown that our system achieved good results (63.7% F1-score) compared to related research in this field.