Mining Public Opinion on Indian Government Policies using R
Pankaj Verma1, Sanjay Jamwal2

1Pankaj Verma*, Department of Computer Sciences, BGSB University, Rajouri, J&K, India.
2Sanjay Jamwal, Department of Computer Sciences, BGSB University, Rajouri, J&K, India.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1310-1315 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8150019320/2020©BEIESP | DOI: 10.35940/ijitee.C8150.019320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Nowadays, social media monitoring has burgeoned at a very rapid pace, so analyzing social data plays a crucial role in knowing people’s behavior. People share their views regarding trending topics that are occurring around the world. Opinion mining is used for extracting the sentiments from the textual data that are shared by peoples. In this work, we are analyzing Twitter Tweets using sentiment analysis which checks the opinion of people regarding various policies that were announced by the Indian Government. The main objective of the paper is to analyze sentiments of various Indian Government policies (namely Article370, New Motor Vehicles Act 2019, Triple Talaq, Jal Shakti Abhiyan, NRC Assam) on Twitter so that public opinions and views are analyzed. Emotions (anger, trust, fear, anticipation, disgust, sadness, joy, surprise) are analyzed using Emotion-based lexicon technique. Sentiments are classified into two categories (positive and negative) from the emotion lexicon Emo Lex. The paper provides a comparative analysis of these policies and this work can act as feedback from people to the government. R programming is used for implementation and visualization. 
Keywords: Sentiment Analysis, Article370, Triple Talaq, Jal Shakti Abhiyan, NRC, etc.
Scope of the Article: Measurement & Performance Analysis