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.
Recommended citation: Akba, F., Uçan, A., Sezer, E. A., & Sever, H. (2014, July). Assessment of feature selection metrics for sentiment analyses: Turkish movie reviews. In 8th European Conference on Data Mining (Vol. 191, pp. 180-184).