Computational Vision and Bio Inspired Computing pp 227-237 | Cite as
Sentiment Analysis of Twitter Data on Demonetization Using Machine Learning Techniques
Abstract
Social media like twitter and Facebook is seen as a space where public opinions are formed in today’s world. The data from these tweets and posts can provide valuable insights for policy makers and other agencies to propose and implement policies better. An attempt is made in this paper to understand the public opinion on the recently implemented demonetization policy in India. A sentiment analysis is carried out on twitter data set using machine learning approaches. Twitter data from November 9th to December 3rd is considered for analysis. The data set is pre-processed for cleaning the data and making it possible for analysis. A final set of 5000 tweets are analysed using machine learning techniques like SVM, Naïve Bayes classifier and Decision tree and the results are compared.
Keywords
Twitter data Sentiment analysis Machine learning DemonetizationReferences
- 1.Gokulakrishnan, B., Priyanthan, P., Ragavan, T., Prasath, N,, Perera, A.: Opinion mining and sentiment analysis on a Twitter data stream. In: 2012 International Conference on IEEE, Advances in ICT for Emerging Regions (ICTer) (2012)Google Scholar
- 2.Go, A., Bhayani, R., Huang, L.: Twitter Sentiment Classification Using Distant Supervision. Technical Report (2009)Google Scholar
- 3.Sanders, N.: Twitter Sentiment Corpus. http://www.sananalytics.com/lab/twitter-sentiment/. Sanders Analytics
- 4.Sun, S., Luo, C., Chen, J.: A review of natural language processing techniques for opinion mining systems. Inf. Fusion 36, 10–25 (2017)CrossRefGoogle Scholar
- 5.Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. European Language Resources Association(ELRA), Valletta Malta (2010)Google Scholar
- 6.Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the Omg! ICWSM 538–541 (2011)Google Scholar
- 7.Saif, H., He. Y., Alani, H.: Semantic Sentiment Analysis of Twitter. In: The Semantic Web-ISWC 2012, pp. 508–524. Springer, Berlin (2012)Google Scholar
- 8.Councill, I.G., McDonald, R., Velikovich. L: What’s great and what’s not: learning to classify the scope of negation for improved sentiment analysis. In: Proceedings of the Workshop on Negation and Speculation in Natural Language Processing, pp. 51–59. Association for Computational Linguistics (2010)Google Scholar
- 9.Peng, Y., Moh, M., Moh, T.S.: Efficient adverse drug event extraction using twitter sentiment analysis. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2016)Google Scholar
- 10.Bao, Y., Quan, C., Wang, L., Ren, F.: The role of preprocessing in Twitter sentiment analysis. Intell. Comput. Methodologies 615–634 (2014)Google Scholar
- 11.Smirnov, I.: Overview of Stemming Algorithms. Mechanical Translation (2008)Google Scholar
- 12.Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)Google Scholar
- 13.NLTK (Nature Language Tool Kit).: Last Retrieved on 21 March 2015 from http://www.nltk.org/
- 14.Bird, S., Klein, E., Loper, E.: Natural language processing with Python. O’Reilly Media, Inc. (2009)Google Scholar
- 15.Nivedhitha, E., Sanjay, S.P., Anand Kumar, M., Soman, K.P.: Unsupervised word embedding based polarity detection for tamil tweets. Int. J. Control Theor. Appl. 9, 4631–4638 (2016)Google Scholar
- 16.Quinlan, J.R..: C4. 5: Programs for Machine Learning, vol. 1. Morgan Kaufmann, Burlington (1993)Google Scholar
- 17.Reshma, U., Barathi Ganesh, H.B., Anand Kumar, M., Soman, K.P.: Supervised methods for domain classification of tamil documents. ARPN J. Eng. Appl. Sci. 10(8), 3702–3707 (2015)Google Scholar
- 18.Seshadri, S., Madasamy, A.K., Padannayil, S.K., Anand Kumar, M.: Analyzing sentiment in indian languages micro text using recurrent neural network. IIOAB J. 7, 313–318 (2016)Google Scholar