Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2016

Abstract

Sentiment analysis is one of the most popular natural language processing techniques. It aims to identify the sentiment polarity (positive, negative, neutral or mixed) within a given text. The proper lexicon knowledge is very important for the lexicon-based sentiment analysis methods since they hinge on using the polarity of the lexical item to determine a text's sentiment polarity. However, it is quite common that some lexical items appear positive in the text of one domain but appear negative in another. In this paper, we propose an innovative knowledge building algorithm to extract sentiment lexicon knowledge through computing their polarity value based on their polarity distribution in text dataset, such as in a set of domain specific reviews. The proposed algorithm was tested by a set of domain microblogs. The results demonstrate the effectiveness of the proposed method. The proposed lexicon knowledge extraction method can enhance the performance of knowledge based sentiment analysis.

Keywords

Weibo, Chinese microblog, Domain knowledge building, Lexicon knowledge extraction, Natural Language Processing, Sentiment analysis

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

16th IEEE International Conference on Data Mining Workshops: 12-15 December, Barcelona, Spain: Proceedings

First Page

978

Last Page

983

ISBN

9781509054725

Identifier

10.1109/ICDMW.2016.0142

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/ICDMW.2016.0142

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