Sentiment analysis of Movie reviews and Blog posts

Author

Singh, V.K. ; Piryani, R. ; Uddin, Ahsan ; Waila, P.

Author_Institution

Dept. of Comput. Sci., South Asian Univ., Delhi, India

fYear

2013

fDate

22-23 Feb. 2013

Firstpage

893

Lastpage

898

Abstract

This paper presents our experimental work on performance evaluation of the SentiWordNet approach for document-level sentiment classification of Movie reviews and Blog posts. We have implemented SentiWordNet approach with different variations of linguistic features, scoring schemes and aggregation thresholds. We used two pre-existing large datasets of Movie Reviews and two Blog post datasets on revolutionary changes in Libya and Tunisia. We have computed sentiment polarity and also its strength for both movie reviews and blog posts. The paper also presents an evaluative account of performance of the SentiWordNet approach with two popular machine learning approaches: Naïve Bayes and SVM for sentiment classification. The comparative performance of the approaches for both movie reviews and blog posts is illustrated through standard performance evaluation metrics of Accuracy, F-measure and Entropy.

Keywords

Web sites; document handling; information analysis; learning (artificial intelligence); pattern classification; support vector machines; F-measure metric; Libya; SVM learning approach; SentiWordNet approach; Tunisia; accuracy metric; aggregation threshold; blog post; document-level sentiment classification; entropy metric; linguistic feature; machine learning approach; movie review; naive Bayes learning approach; scoring scheme; sentiment analysis; sentiment polarity; support vector machines; Accuracy; Blogs; Entropy; Feature extraction; Motion pictures; Sentiment analysis; Support vector machines; Blog Sentiment; Machine Learning Classifiers; SentiWordNet; Sentiment Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Advance Computing Conference (IACC), 2013 IEEE 3rd International

Conference_Location

Ghaziabad

Print_ISBN

978-1-4673-4527-9

Type

conf

DOI

10.1109/IAdCC.2013.6514345

Filename

6514345