ASPECT EXTRACTION AND TWITTER SENTIMENT CLASSIFICATION BY FRAGMENT RULES

The paper deals with approaches to explicit aspect extraction from user reviews of restaurants and sentiment classification of Twitter messages of telecommunication companies based on fragment rules. This paper presents fragment rule model to sentiment classification and explicit aspect extraction. Rules may be constructed manually by experts and automatically by using machine learning procedures. We propose machine learning algorithm for sentiment classification which uses terms that are made by fragment rules and some rule based techniques to explicit aspect extraction including a method based on filtration rule generation. The article presents the results of experiments on a test set for twitter sentiment classification of telecommunication companies and explicit aspect extraction from user review of restaurant. The paper compares the proposed algorithms with baseline and the best algorithm to track. Training sets, evaluation metrics and experiments are used according to SentiRuEval. As our future work, we can point out such directions as: applying semi-supervised methods for rule generation to reduce the labor cost, using active learning methods, constructing a visualization system for rule generation, which can provide the interaction process with experts.