IWD Based Feature Selection Algorithm for Sentiment Analysis

Tuba Parlar,Esra Sarac

doi:10.5755/j01.eie.25.1.22736

Abstract

Feature selection methods aim to improve the classification performance by eliminating non-valuable features. In this paper, our aim is to apply a recent optimization technique namely the Intelligent Water Drops (IWD) algorithm to select best features for sentiment analysis. We investigate the classification performances of our proposed IWD based feature selection method by comparing one of the well-known feature selection method using Maximum Entropy classifier. Experimental results show that Intelligent Water Drops based feature selection method outperforms than ReliefF method for sentiment analysis.DOI: 10.5755/j01.eie.25.1.22736

Highlights

Today internet and social media have become an important source of information
Turkish Twitter data is used and we investigate the effects of our proposed Intelligent Water Drops (IWD) based feature selection method by comparing traditional feature selection method for sentiment analysis
In a previous study on cyberbullying which is a special category of sentiment analysis, using emoticons as features is not effected classification performance well [24]

Summary

Introduction

Today internet and social media have become an important source of information. The increasing amount of information on the internet has brought new research areas. With the increasing importance of sentiment information, there is a need for fast and effective analysis techniques that are subject-oriented and sentiment focused [1]. Sentiment analysis has become an important research area for automatic analysis of review documents. Sentiment analysis aims to classify the sentiments of these review documents especially into two classes: positive or negative. In order to increase the performance of the classification process, feature selection methods are applied to determine the most valuable features. Feature selection is very important in two respects: the efficiency of the training process increases significantly by reducing the number of features; secondly, the accuracy of classification increases by choosing most valuable features

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Conclusion