Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification, By Rahim Dehkharghani

Research

Title	Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification
Type	Presentation
Keywords	opinion mining; sentiment analysis; polarity extraction; SentiWordNet; lexicon based methods; machine learning;
Researchers	Rahim Dehkharghani، Berrin Yanikoglu، Dilek Tapucu، Yucel Saygin

Abstract

Sentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new subjectivity-based features for sentiment classification. The subjectivity-based features we experiment with are based on the average word polarity and the new features that we propose are based on the occurrence of subjective words in review texts. Experimental results on hotel and movie reviews show an overall accuracy of about 84% and 71% in hotel and movie review domains respectively; improving the baseline using just the average word polarities by about 2% points.