Abstract

Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of sentiment polarity categorization, which is one of the fundamental problems of sentiment analysis. A general process for sentiment polarity categorization is proposed with detailed process descriptions. Data used in this study are online product reviews collected from Amazon.com. Experiments for both sentence-level categorization and review-level categorization are performed with promising outcomes. At last, we also give insight into our future work on sentiment analysis.

Details

Title
Sentiment analysis using product review data
Author
Fang Xing 1 ; Zhan, Justin 1 

 North Carolina A&T State University, Department of Computer Science, Greensboro, USA (GRID:grid.261037.1) (ISNI:0000000102874439) 
Publication year
2015
Publication date
Jun 2015
Publisher
Springer Nature B.V.
e-ISSN
21961115
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
1987960670
Copyright
Journal of Big Data is a copyright of Springer, (2015). All Rights Reserved. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.