Efficient Multilevel Polarity Sentiment Classification Algorithm using Support Vector Machine and Fuzzy Logic | Zendy

B. Vamshi Krishna | Zendy; Ajeet Kumar Pandey | Zendy; A.P Siva Kumar | Zendy

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Efficient Multilevel Polarity Sentiment Classification Algorithm using Support Vector Machine and Fuzzy Logic

Author(s) -

B. Vamshi Krishna,

Ajeet Kumar Pandey,

A.P Siva Kumar

Publication year - 2019

Publication title -

international journal of innovative technology and exploring engineering

Language(s) - English

Resource type - Journals

ISSN - 2278-3075

DOI - 10.35940/ijitee.l3772.1081219

Subject(s) - polarity (international relations) , support vector machine , sentiment analysis , computer science , artificial intelligence , set (abstract data type) , inference , machine learning , fuzzy logic , data mining , degree (music) , social media , data set , world wide web , genetics , physics , cell , acoustics , biology , programming language

This paper discusses an efficient algorithm for sentiment classification of online text reviews posted in social networking sites and blogs which are mostly in unstructured and ungrammatical in nature. Model proposed in this paper utilizes support vector machine supervised learning algorithm and fuzzy inference system for enhancing the degree of sentiment polarity of text reviews and providing multilevel polarity categories. Model is also able to predict degree of sentiment polarity of online reviews. The model accuracy is validated on twitter data set and compared with another earlier model.

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