Classification of Sentimental Reviews Using Machine Learning Techniques

Abinash Tripathy*, Ankit Agrawal, Santanu Kumar Rath

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

73 Scopus citations

Abstract

Sentiment Analysis is the most prominent branch of natural language processing. It deals with the text classification in order to determine the intention of the author of the text. The intention can be of admiration (positive) or criticism (Negative) type. This paper presents a comparison of results obtained by applying Naive Bayes (NB) and Support Vector Machine (SVM) classification algorithm. These algorithms are used to classify a sentimental review having either a positive review or negative review. The dataset considered for training and testing of model in this work is labeled based on polarity movie dataset and a comparison with results available in existing literature has been made for critical examination.

Original languageEnglish (US)
Pages (from-to)821-829
Number of pages9
JournalProcedia Computer Science
Volume57
DOIs
StatePublished - 2015
Event3rd International Conference on Recent Trends in Computing, ICRTC 2015 - Delhi, India
Duration: Mar 12 2015Mar 13 2015

Keywords

  • Classification
  • Naive Bayes (NB)
  • Polarity Movie Dataset
  • Sentiment Analysis
  • Support Vector Machine (SVM)

ASJC Scopus subject areas

  • Computer Science(all)

Fingerprint Dive into the research topics of 'Classification of Sentimental Reviews Using Machine Learning Techniques'. Together they form a unique fingerprint.

Cite this