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Polarity Shift Handling Techniques: A Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 158 - Number 6 |
| Year of Publication: 2017 |
| Authors: Daya Mary Mathew, Hema Krishnan |
10.5120/ijca2017912802
|
Daya Mary Mathew, Hema Krishnan . Polarity Shift Handling Techniques: A Survey. International Journal of Computer Applications. 158, 6 ( Jan 2017), 1-5. DOI=10.5120/ijca2017912802
Abstract
This paper presents a survey on sentiment analysis with respect to the polarity shifting problem. Sentiment Analysis is one of the most widely researched applications of Natural Language Processing. The opinions mostly expressed in social networking sites can be harnessed through automated methods using sentiment analysis. Polarity classification is the most classical sentiment analysis task which aims at classifying reviews into either positive, negative or neutral. Polarity shifting is a challenge to sentiment classification and is considered as one of the main reasons why the standard machine learning algorithms make inaccurate predictions. In this paper various techniques to handle the polarity shift problem are explained. A comparitive study is done on these techniques and the classification performance of each technique is explained.
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