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Title:      ASSESSMENT OF FEATURE SELECTION METRICS FOR SENTIMENT ANALYSES: TURKISH MOVIE REVIEWS
Author(s):      F?rat Akba, Alaettin Uçan, Ebru Akcapinar Sezer, Hayri Sever
ISBN:      978-989-8704-10-8
Editors:      Ajith P. Abraham, Antonio Palma dos Reis and Jörg Roth
Year:      2014
Edition:      Single
Keywords:      Sentiment Analyses, Feature Selection, Support Vector Machine, Naïve Bayes, Turkish Corpus.
Type:      Short Paper
First Page:      180
Last Page:      184
Language:      English
Cover:      cover          
Full Contents:      click to dowload Download
Paper Abstract:      Sentiment analysis systems pursuit the goal of detecting emotions in a given text with machine learning approaches. These texts might include three kinds of emotions such as positive, negative and neutral. Entertainment oriented texts, especially movie reviews, contain huge amount of possible emotional information. In this study, we aimed to represent each movie reviews by using small number of features. For this purpose, information gain, chi-square methods have been implemented to extract features for decreasing costs of calculations and increasing success rate. In experiments, employed corpus includes Turkish movie reviews, support vector machine and naïve bayes had been employed for classification and F1 score was used for performance evaluation. According to the experimental results, support vector machine achieved 83.9% performance value while classification of movie reviews in two (positive and negative) categories and also we obtained the 63.3% performance value while classification with support vector machine into three categories.
   

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