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Efficient Sentiment Analysis using Optimal Feature and Bayesian Classifier

by Neha Gupta, Shabnam Parveen
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 145 - Number 8
Year of Publication: 2016
Authors: Neha Gupta, Shabnam Parveen
10.5120/ijca2016910722

Neha Gupta, Shabnam Parveen . Efficient Sentiment Analysis using Optimal Feature and Bayesian Classifier. International Journal of Computer Applications. 145, 8 ( Jul 2016), 10-14. DOI=10.5120/ijca2016910722

@article{ 10.5120/ijca2016910722,
author = { Neha Gupta, Shabnam Parveen },
title = { Efficient Sentiment Analysis using Optimal Feature and Bayesian Classifier },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 145 },
number = { 8 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 10-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume145/number8/25297-2016910722/ },
doi = { 10.5120/ijca2016910722 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:48:14.783455+05:30
%A Neha Gupta
%A Shabnam Parveen
%T Efficient Sentiment Analysis using Optimal Feature and Bayesian Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 145
%N 8
%P 10-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment analysis refers to a broad range of fields of the natural language processing, computational linguistics, and text mining. Mining is used to extract previously unknown information from the different written resources. This extracted information is helping in decision making process. Sentiment analysis has gained much attention in recent years. It determines the opinion and attitude of the people towards a particular topic. This paper focuses to improve the accuracy by using the optimal feature and reduces the complexity by Naïve Bayes classifier. In proposed work, comparing the results with the existing model regarding the accuracy, precision, recall and f-measure which shows that performance are improved in each and every case.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment analysis Natural language processing Optimal feature Naïve Bayes classifier.

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