Learning-Based Method with Valence Shifters for Sentiment Analysis

Document Type

Conference Proceeding

Publication Date

12-15-2017

Abstract

Automatic sentiment classification is becoming a popular and effective way to help online users or companies process and make sense of customer reviews. In this article, a learning-based method for classification of online reviews that achieves better classification accuracy is obtained by (a) combining valence shifters and opinion words into bigrams for use as features in an ordinal margin classifier and (b) using relational information between unigrams/bigrams in the form of a graph to constrain the parameters of the classifier. By using these two components, it is possible to extract more information present in the unstructured data than other methods such as support vector machines and random forest, hence gaining the potential of better classification performance. Indeed, our simulation results show a higher classification accuracy on empirical real data with ground truth and on simulated data.

Identifier

85044094352 (Scopus)

ISBN

[9781538614808]

Publication Title

IEEE International Conference on Data Mining Workshops Icdmw

External Full Text Location

https://doi.org/10.1109/ICDMW.2017.52

e-ISSN

23759259

ISSN

23759232

First Page

357

Last Page

364

Volume

2017-November

This document is currently not available here.

Share

COinS