International Journal of Business Analytics and Intelligence

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Sentiment Analysis of Swachh Bharat Abhiyan


Affiliations
1 School of Management Studies, Punjabi University, Patiala, Punjab, India
     

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The present paper is about the social media analytics. It is a new tool to analyse the behaviour of the users who use social networking sites and other social sites like blogs, forums etc. Every organisation uses this tool to analyse their customers. Even the government agencies are using these analytical tools to get the feedback of their newly launched missions and their policies. In this paper the sentiment analysis of Swachh Bharat Abhiyan is done with the help of tweets extracted from twitter. Tweets regarding Swachh Bharat Abhiyan are extracted with the help of an open source software R-studio. The geo-locations of tweets are also extracted in the software and the results are plotted on the map of India. The pattern of tweets is analysed and the popularity of the mission is evaluated. The word cloud of the popular and the most used words is also formed in the R-studio software. With the overall analysis, the popularity of the mission is perceived according the regions on the map of India, and the strategies can be applied to popularize the campaign in the lesser known regions of India.

Keywords

Swachh Bharat, Word Cloud, Geo-Location, Campaign.
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  • Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011, June). Sentiment analysis of twitter data. In Proceedings of the Workshop on Languages in Social Media (pp. 30-38). Association for Computational Linguistics.

  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market.Journal of Computational Science, 2(1), 1-8.

  • Bruns, A., & Stieglitz, S. (2013). Towards more systematic Twitter analysis: Metrics for tweeting activities. International Journal of Social Research Methodology, 16(2), 91-108.

  • Ebner, M., Altmann, T., & Softic, S. (2011). @ twitter analysis of# edmedia10–is the# informationstream usable for the# mass. Form@ re-Open Journal per la formazione in rete, 11(74), 36-45.

  • Ediger, D., Jiang, K., Riedy, J., Bader, D. A., Corley, C., Farber, R., & Reynolds, W. N. (2010, September). Massive social network analysis: Mining twitter for social good. In Parallel Processing (ICPP), 2010 39th International Conference on (pp. 583-593). IEEE.

  • Go, A., Bhayani, R., & Huang, L. (2009a). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1-12.

  • Go, A., Huang, L.,&Bhayani;, R. (2009b). Twitter sentiment analysis. Entropy, 17.

  • Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U., Keim, D. A., Haug, L., & Hsu, M. C. (2011, October). Visual sentiment analysis on twitter data streams. In Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on (pp. 277-278). IEEE.

  • Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the omg!. ICWSM, 11, 538-541.

  • Li, R., Lei, K. H., Khadiwala, R., & Chang, K. C. (2012, April). Tedas: A twitter-based event detection and analysis system. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on (pp. 12731276). IEEE.

  • Mathioudakis, M., & Koudas, N. (2010, June).Twitter monitor: Trend detection over the twitter stream. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data (pp. 1155-1158).ACM.

  • Naveed, N., Gottron, T., Kunegis, J., &Alhadi;, A. C. (2011, June). Bad news travel fast: A content-based analysis of interestingness on twitter. InProceedings of the 3rd International Web Science Conference (p. 8).ACM.

  • Pak, A., &Paroubek;, P. (2010, May).Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In LREC.

  • Saif, H., He, Y., &Alani;, H. (2012).Alleviating data sparsity for twitter sentiment analysis. CEUR Workshop Proceedings (CEUR-WS. org).

  • Thomas, K., Grier, C., Song, D., & Paxson, V. (2011, November). Suspended accounts in retrospect: An analysis of twitter spam. In Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference (pp. 243-258).ACM.

  • Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. ICWSM, 10, 178-185.

  • Wang, X., Wei, F., Liu, X., Zhou, M., & Zhang, M. (2011, October). Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1031-1040). ACM.

  • Yardi, S., & Boyd, D. (2010). Dynamic debates: an analysis of group polarization over time on Twitter. Bulletin of Science, Technology & Society, 30(5), 316-327.

  • Zielinski, A., Bugel, U., Middleton, L., Middleton, S. E., Tokarchuk, L., Watson, K., & Chaves, F. (2012, April). Multilingual analysis of twitter news in support of mass emergency events. In EGU General Assembly Conference Abstracts (Vol. 14, p. 8085).


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  • Sentiment Analysis of Swachh Bharat Abhiyan

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Authors

Sahil Raj
School of Management Studies, Punjabi University, Patiala, Punjab, India
Tanveer Kajla
School of Management Studies, Punjabi University, Patiala, Punjab, India

Abstract


The present paper is about the social media analytics. It is a new tool to analyse the behaviour of the users who use social networking sites and other social sites like blogs, forums etc. Every organisation uses this tool to analyse their customers. Even the government agencies are using these analytical tools to get the feedback of their newly launched missions and their policies. In this paper the sentiment analysis of Swachh Bharat Abhiyan is done with the help of tweets extracted from twitter. Tweets regarding Swachh Bharat Abhiyan are extracted with the help of an open source software R-studio. The geo-locations of tweets are also extracted in the software and the results are plotted on the map of India. The pattern of tweets is analysed and the popularity of the mission is evaluated. The word cloud of the popular and the most used words is also formed in the R-studio software. With the overall analysis, the popularity of the mission is perceived according the regions on the map of India, and the strategies can be applied to popularize the campaign in the lesser known regions of India.

Keywords


Swachh Bharat, Word Cloud, Geo-Location, Campaign.

References