Feature selection methods in sentiment analaysis

In today's technology, people are starting to share their opinions, ideas and feelings through many mediums because the internet is used extensively by every segment. These shares have become an important source of work on sentiment analysis and have led to increased work on this field. The sen...

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Bibliographic Details
Published in2017 International Artificial Intelligence and Data Processing Symposium (IDAP) pp. 1 - 5
Main Authors Kaynar, Oguz, Arslan, Halil, Gormez, Yasin, Demirkoparan, Ferhan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Feature extraction
Features selection
Indexes
Motion pictures
Mutual information
Niobium
Sentiment analysis
Support vector machine
Support vector machines
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Summary:In today's technology, people are starting to share their opinions, ideas and feelings through many mediums because the internet is used extensively by every segment. These shares have become an important source of work on sentiment analysis and have led to increased work on this field. The sentiment analysis is simply to determine whether the emotion is included or not, and to determine whether the emotion is positive, negative, or neutral. In this study, chi-square, information gain, gain ratio, gini coefficient, oneR and reliefF methods are applied on the data sets according to the contents of movie comments and the obtained data sets are classified by Support Vector Machines (SVM). As a result of the application, it has been observed that the feature selection methods improve the results of sentiment analysis.
DOI:10.1109/IDAP.2017.8090187