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A Hybrid Approach for Polarity Shift Detection


Affiliations
1 Department of Information Technology, G.H. Patel College of Engineering and Technology, India
     

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Now-a-days sentiment analysis has become a hot research area. With the increasing use of internet, people express their views by using social media, blogs, etc. So there is a dire need to analyze people's opinions. Sentiment classification is the main task of sentiment analysis. But while classifying sentiments, the problem of polarity shift occurs. Polarity shift is considered as a very crucial problem. Polarity shift changes a text from positive to negative and vice versa. In this paper, a hybrid approach is proposed for polarity shift detection of negation (explicit and implicit) and contrast. The hybrid approach consists of a rule-based approach for detecting explicit negation and contrast and a lexicon called SentiWordNet for detecting implicit negation. The proposed approach outperforms its baselines.

Keywords

Sentiment Analysis, Sentiment Classification, Polarity Shift, Natural Language Processing, Lexicon.
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  • A Hybrid Approach for Polarity Shift Detection

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Authors

Michele Mistry
Department of Information Technology, G.H. Patel College of Engineering and Technology, India
Prem Balani
Department of Information Technology, G.H. Patel College of Engineering and Technology, India

Abstract


Now-a-days sentiment analysis has become a hot research area. With the increasing use of internet, people express their views by using social media, blogs, etc. So there is a dire need to analyze people's opinions. Sentiment classification is the main task of sentiment analysis. But while classifying sentiments, the problem of polarity shift occurs. Polarity shift is considered as a very crucial problem. Polarity shift changes a text from positive to negative and vice versa. In this paper, a hybrid approach is proposed for polarity shift detection of negation (explicit and implicit) and contrast. The hybrid approach consists of a rule-based approach for detecting explicit negation and contrast and a lexicon called SentiWordNet for detecting implicit negation. The proposed approach outperforms its baselines.

Keywords


Sentiment Analysis, Sentiment Classification, Polarity Shift, Natural Language Processing, Lexicon.

References