IMPROVING PERFORMANCE OF TWITTER BASED ON SENTIMENT ANALYSIS

Authors

  • Nikhil Kalraiya Infinity Management & Engineering College Sagar (M.P.),RGPV,Bhopal,Sagar (M.P.), India
  • Asst.Prof Sarvesh Rai Infinity Management & Engineering College ,RGPV,Bhopal,Sagar (M.P.), India

Keywords:

Sentiment Analysis, Opinion Mining, Natural Language Processing

Abstract

Social networking websites, application such as Twitter and Facebook, Instagram are important spaces for discussion regarding anything like product, events, election etc. Now a day the channels for shows opinions seem to increase daily. When these opinions are applicable to a company, These opinions are important sources of business insight, whether they represent critical intelligence about a customer‟s defection risk, the impact of an strong reviewer on other people‟s obtain decisions, or early feedback on product releases, company news or competitors. The importance of controlling the opinion is growing as Customer use technologies such as Twitter to express their views directly to other customer. This was the main encouragement behind this work. It is decided to develop a system that cans analyses about Demonetisation in India 2016.

References

Sanket Sahu, Suraj Kumar Rout, Debasmit Mohanty “Twitter Sentiment Analysis” 2015 IEEE International Symposium on Nanoelectronic and Information Systems. [2]Walaa Medhat , Ahmed Hassan , Hoda Korashy “Sentiment analysis algorithms and applications:A survey”Ain Shams Engineering Journal (2014) 5, 1093–1113 2090-4479 _ 2014 Production and hosting by Elsevier B.V. on behalf of Ain Shams University. http://dx.doi.org/10.1016/j.asej.2014.04.011.

Dr Nirmala C R, Roopa G M, Naveen Kumar K R “Twitter Data Analysis for Unemployment Crisis”978-1-4673-9223-5/15/$31.00_c 2015 IEEE

Lorenzo Gatti, Marco Guerini, and Marco Turchi ” SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis”This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TAFFC.2015.2476456, IEEE Transactions On Affective Computing Journal Of Latex Class Files, VOL. 6, NO. 1, JANUARY 2007

Neha Raghuvanshi, Prof. J.M. Patil “A Brief Review on Sentiment Analysis” International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – 2016 978-1-4673-9939-5/16/$31.00 ©2016 IEEE

Mr. S. M. Vohra, Prof. J. B. Teraiya “A Comparative Study Of Sentiment Analysis Techniques” Journal Of Information, Knowledge And Research In Computer Engineering Issn: 0975 – 6760| Nov 12 To Oct 13 | Volume – 02, Issue – 02

Prajwala T R “A Comparative Study on Decision Tree and Random Forest Using R Tool”International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 1, January 2015

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Published

31-12-2016

How to Cite

Nikhil Kalraiya, & Asst.Prof Sarvesh Rai. (2016). IMPROVING PERFORMANCE OF TWITTER BASED ON SENTIMENT ANALYSIS. International Journal for Research Publication and Seminar, 7(9). Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/994

Issue

Vol. 7 No. 9 (2016): Oct - Dec 2016

Section

Original Research Article

License

Copyright (c) 2016 International Journal for Research Publication and Seminar

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