Twitter Feature Selection and Classification Using Support Vector Machine for Aspect-Based Sentiment Analysis

Abstract

In this paper, with regards to aspect-based sentiment classification accuracy problem, we propose a Principal Component Analysis (PCA) feature selection method that can determine the most relevant set of features for aspect-based sentiment classification. Feature selection helps to reduce redundant features and remove irrelevant features which affect classifier accuracy. In this paper we present a method for feature selection for twitter aspect-based sentiment classification based on Principal Component Analysis (PCA). PCA is combined with Sentiwordnet lexicon-based method which is incorporated with Support Vector Machine (SVM) learning framework to perform the classification. Experiments on our own Hate Crime Twitter Sentiment (HCTS) and benchmark Stanford Twitter Sentiment (STS) datasets yields accuracies of 94.53 % and 97.93 % respectively. The comparisons with other statistical feature selection methods shows that our proposed approach shows promising results in improving aspect-based sentiment classification performance.