Efficient multilevel polarity sentiment classification algorithm using support vector machine and fuzzy logic

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

doi:10.35940/ijitee.L3772.1081219

Efficient multilevel polarity sentiment classification algorithm using support vector machine and fuzzy logic

B. Vamshi Krishna^*, Ajeet Kumar Pandey, A. P. Siva Kumar

^*Corresponding author for this work

School of Computer Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	5048-5051
Number of pages	4
Journal	International Journal of Innovative Technology and Exploring Engineering
Volume	8
Issue number	12
DOIs	https://doi.org/10.35940/ijitee.L3772.1081219
Publication status	Published - 10-2019

All Science Journal Classification (ASJC) codes

General Computer Science
Civil and Structural Engineering
Mechanics of Materials
Electrical and Electronic Engineering

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10.35940/ijitee.L3772.1081219

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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.",

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AU - Pandey, Ajeet Kumar

AU - Siva Kumar, A. P.

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PY - 2019/10

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N2 - 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.

AB - 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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ER -

Efficient multilevel polarity sentiment classification algorithm using support vector machine and fuzzy logic

Abstract

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