Twitter Sentiment Analysis on Indian Government Project using R.
Pankaj Verma1, Akib Mohi-Ud-Din Khanday2, Syed Tanzeel Rabani3, Mahmood Hussain Mir4
Sanjay Jamwal5
1Pankaj Verma, MPhil Scholar, Department of Computer Sciences, BGSBU Rajouri, J&K, India.
2Akib Mohi-Ud-Din Khanday, Ph.D. Scholar, Department of Computer Sciences, BGSBU Rajouri, J&K, India.
3Syed Tanzeel Rabani. Ph.D. Scholar, Department of Computer Sciences, BGSBU Rajouri, J&K.
4Mahmood Hussain Mir, Ph.D. scholar, department of Computer Sciences, BGSBU Rajouri, J&K.
5Dr. Sanjay Jamwal, Professor, Department of Computer Sciences, BGSBU Rajouri, J&K, India.
Manuscript received on 02 August 2019. | Revised Manuscript received on 08 August 2019. | Manuscript published on 30 September 2019. | PP: 8338-8341 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6612098319/2019©BEIESP | DOI: 10.35940/ijrte.C6612.098319
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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: The main motive behind this research paper is to use the power of social media to observe, examine and analyze the opinion regarding recent Indian government project as the opinion of people plays a vital role in formulating the government policies. By getting into the deeper insights on social media, one can easily analyze the behavior of people regarding various issues and policies, which was otherwise impossible using traditional sources. The case study was done on Statue of Unity. Analysis was done on one of the famous social networking sites i.e. Twitter, using R programming language. Twitter API was used to collect the primary data. Tweets were analyzed by using opinion lexicon and Emotion lexicon-based approaches. Opinion Lexicon based approach categorized the sentiment of tweets in three categories, while Emotion Lexicon based approach refined them into eight more categories. The research work done in this paper will help government to understand the emotions of people regarding their policies and will also enrich people to help them understand majority vote of people.
Keywords: Sentiment Analysis, Statue of Unity, Twitter API, Twitter Data.
Scope of the Article: Predictive Analysis