Efficient Multilevel Polarity Sentiment Classification Algorithm using Support Vector Machine and Fuzzy Logic
Vamshi Krishna. B1, Ajeet Kumar Pandey2, Siva Kumar A.P3

1B. Vamshi Krishna*, Research Scholar, Dept. of CSE, JNTUA, Anantapuramu, India.
2Dr. Ajeet Kumar Pandey, Delivery Manager, Dept. of RAMS, L&T Technology Services, Bangalore India.
3Dr. A.P Siva Kumar, Asst. Professor, Dept. of CSE, JNTUA, Anantapuramu, India. 

Manuscript received on September 17, 2019. | Revised Manuscript received on 28 September, 2019. | Manuscript published on October 10, 2019. | PP: 5048-5051 | Volume-8 Issue-12, October 2019. | Retrieval Number: L37721081219/2019©BEIESP | DOI: 10.35940/ijitee.L3772.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Opinion mining, Sentiment Classification, Machine learning, Fuzzy logic.
Scope of the Article: Classification