Feature based sentiment analysis on informal Turkish texts

Kama, Batuhan
Sentiment analysis (SA); in other words, opining mining, is the automatic extraction process of a author' s feeling on a specific topic. These feelings can be positive, negative or neutral. However, most of the time authors do not carry the same opinion for all parts of a topic. While they have a positive attitude on some parts of a topic, they can criticise other parts. Therefore, feature or aspect based sentiment analysis (FBSA) a specialized version of sentiment analysis is used to analyse people' s attitude on each specific feature of a topic. Nowadays, there are a lot of forums, blogs and review sites where customers and professional reviewers add their comments on different products. Since thousands of new entries are written to these sites each day, it is almost impossible to read all comments on a product and its features and learn powerful and weak sides of this product; and hence, the need for classifying these informal online data automatically has increased and FBSA can be used to fulfill this need. Up to date, most of the studies on both sentiment analysis and feature based sentiment analysis was on English. There are only several works on these topic on Turkish. In this thesis, a dataset created by collecting comments and reviews from forums is processed with existing feature based sentiment analysis methods for English. Moreover, new methods for Turkish feature based sentiment analysis is proposed. 


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Citation Formats
B. Kama, “Feature based sentiment analysis on informal Turkish texts,” M.S. - Master of Science, Middle East Technical University, 2016.