IJBTT

Text Sentiment Analysis Using Naïve Baye’s Classifier
	© 2020 by IJCTT Journal
	Volume-68 Issue-4
	Year of Publication : 2020
	Authors : Afreen Jaha, N.Satya Deepthi, G.Suryakanth,G. Surya Kala Eswari
	DOI :  10.14445/22312803/IJCTT-V68I4P141

How to Cite?

Afreen Jaha, N.Satya Deepthi, G.Suryakanth,G. Surya Kala Eswari, "Text Sentiment Analysis Using Naïve Baye’s Classifier," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 261-265, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I4P141

Abstract
Sentiment analysis may be a study that analyses people`s opinions, sentiments, evaluations, attitudes, and emotions from written communication. It`s one among the foremost active research areas in tongue processing and is additionally widely studied in processing, Web mining, and text mining. This research has pervaded outside of computing to the management sciences and social sciences to business and society as a whole. The evolving importance of sentiment analysis coincides with the expansion of social media like reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the primary time in human history, we now have an enormous volume of opinionated data recorded in digital form for analysis. Today Sentiment analysis is being applied in most of the businesses and social domains because opinions are key influencers to most human activities and behaviours. Our beliefs and perceptions of reality, and thus the alternatives we make, are largely conditioned on how others see and evaluate the earth. For this reason, once we need to make a choice we often seek out the opinions of others. This is often true not only for individuals but also for organizations. In this paper we use Naïve Bayes classifiers in determining the sentiment embedded within the textual data.

Keywords
Sentiment Analysis, Naïve Bayes Algorithm, Natural Language Toolkit

Reference
[1] M. Vadivukarassi, N. Puviarasan and P. Aruna, “Sentimental Analysis of Tweets Using Naive Bayes Algorithm”, [Publication Details: World Applied Sciences Journal 35 (1): 54-59, 2017, DOI: 10.5829/idosi.wasj.2017.54.59]
[2] Ankur Goel, Jyoti Gautam; Sitesh Kumar, “Sentimental Analysis of Tweets Using Naive Bayes Algorithm”, [Publication Details: 2016 2nd International Conference on Next Generation Computing Technologies (NGCT), DOI:10.1109/NGCT.2016.7877424]
[3] Vik Paruchuri, “Predicting Movie Review Sentiment with Naive Bayes”, [blog:https://www.dataquest.io/blog/naive-bayes-tutorial/]
[4] Filip Knyszewski Machine teacher @ University of Amsterdam Linkedin:https://www.linkedin.com/in/filip-knyszewski-11141fk/
[5] HumaParveen and ShikhaPandey “Sentiment analysis on Twitter Dataset using Naive Bayes algorithm” 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) page 416-419 @article{Parveen2016SentimentAO}.
[6] http://www.international journalssrg.org/ssrg-journals.html
[7] Le B., Nguyen H. (2015) “Twitter Sentiment Analysis Using Machine Learning Techniques”. In: Le Thi H., Nguyen N., Do T. (Eds) Advanced Computational Methods for Knowledge Engineering. Advances in Intelligent Systems and Computing, vol 358. Springer, Cham
[8] Sayali P. Nazare, Prasad S. Nar, Akshay S. Phate, Prof.Dr. D. R. Ingle “Sentiment Analysis in Twitter” International Research Journal of Engineering and Technology (IRJET) Volume: 05, Jan-2018.
[9] Anuja Prakash Jain and Padma Dandannavar “Application of machine learning techniques to sentiment analysis” 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) pages 628-632 article {Jain2016ApplicationOM}
[10] Mejova, Yelena. (2019) “Sentiment Analysis: An Overview”.