Abstract
Sentiment analysis has become a very useful tool in recent times for studying people’s opinions, sentiments and subjective evaluation of any event of social and economic relevance, and in particular, policy decisions. The present paper proposes a framework for sentiment analysis using twitter data for the ’demonetization’ effort of the Government of India. The paper employs twitter data using Twitter API. The methodology of the paper involves collection of data from twitter from different cities of India using geolocation and preprocessing followed by a lexicon-based approach to analyse users’ sentiments over a period of five weeks preceding the policy announcement. In addition to this, the paper also attempts to analyse the sentiments of specific groups of people representing diverse interest groups.
Similar content being viewed by others
Notes
The Gazette of India Extraordinary, part II-Section 3-Sub-section (ii), Ministry of Finance (New Delhi: Controller of Publications, Government of India, and November 8, 2016).
A joint study conducted by National Investigation Agency and Indian Statistical Institute estimates the value of fake currency notes to the tune of at least Rs. 4000 million between 2010–11 and 2014–15 (Rahul Tripathi, The Economic Times, November 15, 2016). Also see the replies to Lok Sabha Starred Question Number 41 on 18/11/2016, which puts the value of actual seizure of counterfeit notes between 2013 and September 2016 at Rs. 1550 million. Another Rajya Sabha Unstarred Question Number 3777 on 13th August 2014, Ministry of Home Affairs, reports that between 2011 and 30th June 2014 the total amount of seized currency notes is Rs. 838 million. The National Investigation Agency puts the value of fake currency notes at Rs. 160,000 million (The Indian Express, June 11, 2012).
References
Sharma A and Dey S 2012 A comparative study of feature selection and machine learning techniques for sentiment analysis. In: Proceedings of the ACM Research in Applied Computation Symposium, pp. 1–7
Wilson T, Wiebe J and Hoffman P 2005 Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of HLT/EMNLP
AitorGarca P, Cuadros M and Rigau G 2018 W2VLDA: almost unsupervised system for aspect based sentiment analysis. Expert Systems with Applications 91: 127–137
Medhat W, Hassan A and Korashy H 2014 Sentiment analysis algorithms and applications: a survey. Ain Shams Engineering Journal 5(4): 1093–1113
Bing L 2010 Sentiment analysis and subjectivity. In: Handbook of natural language processing, 2nd ed. (chapter)
Melville P, Gryc W and Lawrence R D 2009 Sentiment analysis of blogs by combining lexical knowledge with text classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1275–1284
Rajakumar J D and Shetty S L 2016 Demonetisation, the Present and the Aftermath. Economic and Political Weekly 51(48): 13–17
Lahiri A K 2016 Demonetisation and cash shortage. Economic and Political Weekly 51(48): 13–17
Nag S 2016 Lost due to demonetisation. Economic and Political Weekly 51(48): 18
Reddy R C 2017 Demonetisation and black money. Orient BlackSwan
Dasgupta D 2016 Theoretical analysis of ‘Demonetisation’. Economic Political Weekly 51: 51
Serrano J, Francisco P and Herrera-Viedma E 2015 Sentiment analysis: a review and comparative analysis of web services. Information Sciences 311: 18-38.
Chenlo J M and Losada D E 2014 An empirical study of sentence features for subjectivity and polarity classification. Information Sciences 280: 275–288
Araque O, Corcuera I, Fernando Snchez-Rada J and Iglesias C A 2017 Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications 77: 236–246
Tang H, Tan S and Cheng X 2009 A survey on sentiment detection of reviews. Expert Systems with Applications 36(7): 10760–10773
Pandey A C, Rajpoot D S and Saraswat M 2017 Twitter sentiment analysis using hybrid cuckoo search method. Information Processing and Management 53(4): 764-779.
Kouloumpis E, Wilson T and Moore J 2011 Twitter sentiment analysis: the good the bad and the omg!. In: Proceedings of the ICWSM, vol. 11, pp. 538–541
Zhou F, Jiao J R, Yang J and Lei B 2017 Augmenting feature model through customer preference mining by hybrid sentiment analysis. Expert Systems with Applications 89: 306-317
Chatzakou D and Vakali A 2015. Harvesting opinions and emotions from social media textual resources. IEEE Internet Computing 19: 46–50, ISSN 1089-7801
Kolchyna O, Souza T T P, Treleaven P and Aste T 2015 Twitter sentiment analysis: lexicon method, machine learning method and their combination. Department of Computer Science, UCL, Gower Street, London, UK
Chenlo J M and Losada D E 2014 An empirical study of sentence features for subjectivity and polarity classification. Information Sciences 280: 275–288
Kang H, Joo S, Yoo N and Han D 2012 Senti-lexicon and improved Naive Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications 39(5): 6000–6010
Bag S, Tiwari M K and Chan F T S 2017 Predicting the consumer’s purchase intention of durable goods: an attribute-level analysis. Journal of Business Research, https://doi.org/10.1016/j.jbusres.2017.11.031
Wong F M, Wei Tan C, Sen S and Chiang M 2016 Quantifying political leaning from tweets, retweets, and retweeters. Transactions on Knowledge and Data Engineering 28(8): 2158–2172
Kumar A and Sebastian T M 2012 Sentiment analysis on twitter. International Journal of Computer Science Issues 9(4): 372–378
Sailaja K D, Evangelin G and Manoj T V S 2016 Analysing the data from Twitter using R. International Journal of Advanced Research in Computer and Communication Engineering 5(2): 91–93
Flekov L, Ferschk O and Gurevych I 2014 A lexical semantic approach to sentiment polarity prediction in twitter data. In: Proceedings of the 8th International Workshop on Semantic Evaluation, Dublin, Ireland, pp. 704–710
Huang F, Zhang S, Zhang J and Yu G 2017 Multimodal learning for topic sentiment analysis in microblogging. Neurocomputing 253: 144-153
Montejo Rez A, Martnez-Cmara E, Teresa M, Valdivia M, Alfonso L and Lpez U 2014 Ranked WordNet graph for sentiment polarity classification in twitter. Computer Speech & Language 28(1): 93–107
Bose S, Saha U, Kar D, Goswami S, Nayak A K and Chakrabarti S 2017 RSentiment: a tool to extract meaningful insights from textual reviews. In: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications. Singapore: Springer, pp. 259–268
Rao Y, Li O, Mao X and Liu W 2014 Sentiment topic models for social emotion mining. Information Sciences 266: 90–100
Vaidya S and Rafi M 2014 An improved SentiWordNet for opinion mining and sentiment analysis. Journal of Advanced Database Management & Systems, vol. 1, issue 2
Mouthami K, Nirmala Devi K and Murali Bhaskaran V 2013 Sentiment analysis and classification based on textual reviews. In: Proceedings of the International Conference on Information Communication and Embedded Systems (ICICES), IEEE
Havasi C, Speer R, Pustejovsky J and Lieberman H 2009 Digital intuition: applying common sense using dimensionality reduction. IEEE Intelligent Systems 24(4): 24–35
Abbasi A, Chen H and Salem A 2008 Sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Transactions on Information Systems 26(3): 1–34
Bird S, Klein E and Loper E 2009 Natural language processing with Python. O’Reilly Media, Inc.
Pang B and Lee L 2008 Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2: 1–135
JesusSerrano A, Francisco P and EnriqueHerrera V 2015 Sentiment analysis: a review and comparative analysis of web services. Information Sciences 311: 18–38
Jansen B J, Zhang M, Sobel K and Chowdury A 2009 Twitter power: tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology 60(11): 2169–2188
Goswami S, Chakraborty S, Ghosh S, Chakrabarti A and Chakraborty B 2016 A review on application of data mining techniques to combat natural disasters. Ain Shams Engineering Journal, https://doi.org/10.1016/j.asej.2016.01.012
Cambria E, Hussain A, Havasi C and Eckl C Sentic computing: exploitation of common sense for the development of emotion-sensitive systems. In: Lecture Notes in Computer Science, vol. 5967. Berlin–Heidelberg: Springer-Verlag, pp. 148–156
Pang B and Lee L 2004 A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Scott D (Ed.) Proceedings of the ACL. Morristown: ACL, pp. 271–278
Han Y and Ko Kim K 2016 Sentiment analysis on social media using morphological sentence pattern model. In: Software Engineering Research, Management and Applications, Studies in Computational Intelligence. Springer, pp. 79–84
Burak Eliacik A and Erdogan N 2018 Influential user weighted sentiment analysis on topic based microblogging community. Expert Systems with Applications 92: 403-418
Yao T F, Cheng X W, Xu F Y, Uszkoreit H and Rui W 2008 A survey of opinion mining for texts. Journal of Chinese Information Processing 22(03): 71–80
Acknowledgements
Das acknowledges the support from CSE-CSSSC Research Project on Capital Market funded by Calcutta Stock Exchange. However, the view or comment expressed in the paper is personal.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ray, P., Chakrabarti, A., Ganguli, B. et al. Demonetization and its aftermath: an analysis based on twitter sentiments. Sādhanā 43, 186 (2018). https://doi.org/10.1007/s12046-018-0949-0
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12046-018-0949-0