, Volume 32, Issue 4, pp 633–645 | Cite as

Sentiment analysis on social campaign “Swachh Bharat Abhiyan” using unigram method

  • Devendra K. TayalEmail author
  • Sumit K. Yadav
Student Forum


Sentiment analysis is the field of natural language processing to analyze opinionated data, for the purpose of decision making. An opinion is a statement about a subject which expresses the sentiments as well as the emotions of the opinion makers on the topic. In this paper, we develop a sentiment analysis tool namely SENTI-METER. This tool estimates the success rate of social campaigns based on the algorithms we developed that analyze the sentiment of word as well as blog. Social campaigns have a huge impact on the mindset of people. One such campaign was launched in India on October 2, 2014, named Swachh Bharat Abhiyan (SBA). Our tool computes an elaborated analysis of Swachh Bharat Abhiyan, which examines the success rate of this social campaign. Here, we performed the location-wise analysis of the campaign and predict the degree of polarity of tweets along with the monthly and weekly analysis of the tweets. The experiments were conducted in five phases namely extraction and preprocessing of tweets, tokenization, sentiment evaluation of a line, sentiment evaluation of a blog (document) and analysis. Our tool is also capable of handling transliterated words. Unbiased tweets were extracted from Twitter related to this specific campaign, and on comparing with manual tagging we were able to achieve 84.47 % accuracy using unigram machine learning approach. This approach helps the government to implement the social campaigns effectively for the betterment of the society.


Sentiment analysis Lexical analysis SENTI-METER Social campaign Swachh Bharat Abhiyan 


  1. Al-Osaimi S, Badruddin KM (2014) Role of emotion icons in Sentiment classification of Arabic tweets. In: Proceedings of the 6th international conference on management of emergent digital ecosystems, pp 167–171Google Scholar
  2. Alsaleem S (2011) Automated Arabic text categorization using SVM and NB. Int Arab J e-Technol 2(2):124–128Google Scholar
  3. Altheneyan AS, Menai MEB (2014) Naïve Bayes classifiers for authorship attribution of Arabic texts. J King Saud Univ-Comput Inf Sci 26(4):473–484Google Scholar
  4. Baccianella S, Esuli A, Sebastiani F (2010) SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. InLREC 10:2200–2204Google Scholar
  5. Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of the 23rd international conference on computational linguistic, pp 36–44Google Scholar
  6. Benamara F, Cesarano C, Picariello A, Recupero DR, Subrahmanian VS (2007) Sentiment analysis: adjectives and adverbs are better than adjectives alone. ICWSM 26 Mar 2007, pp 1–7Google Scholar
  7. Bertrand KZ, Bialik M, Virdee K, Gros A, Bar-Yam Y (2013) Sentiment in New York City: a high resolution spatial and temporal view. arXiv preprint arXiv:1308.5010
  8. Bhuta S, Doshi U (2014) A review of techniques for sentiment analysis of twitter data. In: 2014 International conference on issues and challenges in intelligent computing techniques (ICICT), 7 Feb 2014. IEEE, pp 583–591Google Scholar
  9. Bosco C, Patti V, Bolioli A (2013) Developing corpora for sentiment analysis: the case of irony and senti-tut. IEEE Intell Syst 1(2):55–63CrossRefGoogle Scholar
  10. Ceron A, Curini L, Lacus SM (2015) Using sentiment analysis to monitor electoral campaigns method matters—evidence from the United States and Italy. Soc Sci Comput Rev 33(1):3–20CrossRefGoogle Scholar
  11. Chew C, Eysenbach G (2010) Pandemics in the age of twitter: content analysis of tweets during the 2009 H1N1 outbreak. PLoS One 5(11):e14118CrossRefGoogle Scholar
  12. Colleoni E, Arvidsson A, Hansen LK, Marchesini A (2011) Measuring corporate reputation using sentiment analysis. In: Proceedings of the 15th international conference on corporate reputation: navigating the reputation economyGoogle Scholar
  13. Dang Y, Zhang Y, Chen H (2010) A lexicon-enhanced method for sentiment classification: an experiment on online product reviews. Intell Syst 25(4):46–53CrossRefGoogle Scholar
  14. Davidov D, Tsur O, Rappoport A (2010) Enhanced sentiment learning using twitter hash tags and smileys. In: Proceedings of the 23rd international conference on computational linguistics, pp 241–249Google Scholar
  15. Desmet B, Hoste V (2013) Emotion detection in suicide notes. Expert Syst with Appl 40(16):6351–6358CrossRefGoogle Scholar
  16. Gupta N, Di Fabbrizio G, Haffner P (2010) Capturing the stars: predicting ratings for service and product reviews. In: Proceedings of the NAACL HLT 2010 workshop on semantic search, pp 36–43Google Scholar
  17. Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N project report stanford. 1: 12Google Scholar
  18. Hai Z, Chang K, Kim JJ, Yang CC (2014) Identifying features in opinion mining via intrinsic and extrinsic domain relevance. IEEE Trans Knowl Data Eng 3:623–634CrossRefGoogle Scholar
  19. Hu N, Bose I, Koh NS, Liu L (2012) Manipulation of online reviews: analysis of ratings, readability, and sentiments. Decis Support Syst 52(3):674–684CrossRefGoogle Scholar
  20. Kim S, Zhang J, Chen Z, Oh A, Liu S (2013) A hierarchical aspect-sentiment model for online reviews. AAAIGoogle Scholar
  21. Li YM, Shiu YL (2012) A diffusion mechanism for social advertising over microblogs. Decis Support Syst 54(1):9–22CrossRefGoogle Scholar
  22. Liu H, He J, Wang T, Song W, Du X (2013) Combining user preferences and user opinions for accurate recommendation. Electron Commer Res Appl 12(1):14–23CrossRefGoogle Scholar
  23. Mahyoub FH, Siddiqui MA, Dahab MY (2014) Building an Arabic sentiment lexicon using semi-supervised learning. J King Saud Univ-Comput Inf Sci 26(4):417–424Google Scholar
  24. Maks I, Vossen P (2012) A lexicon model for deep sentiment analysis and opinion mining applications. Decis Support Syst 53(4):680–688CrossRefGoogle Scholar
  25. Malisiewicz T, Gupta A, Efros AA (2011) Ensemble of exemplar-svms for object detection and beyond. In: Computer vision (ICCV), 2011 IEEE international conference on 6 Nov 2011, pp 89–96Google Scholar
  26. McDonald R, Hannan K, Neylon T, Wells M, Reynar J (2007) Structured models for fine-to-coarse sentiment analysis. In: Annual meeting-association for computational linguistics, vol 45, no. 1, p 432Google Scholar
  27. Mehta Rushabh et al (2012) Sentiment analysis and influence tracking using twitter. Int J Adv Res Comput Sci Electron Eng (IJARCSEE) 1(2):72Google Scholar
  28. Mishne G, De Rijke M (2006) A study of blog search. Advances in information retrieval. Springer, Berlin, pp 289–301CrossRefGoogle Scholar
  29. Moreo A, Romero M, Castro JL, Zurita JM (2012) Lexicon-based comments-oriented news sentiment analyzer system. Expert Syst Appl 39(10):9166–9180CrossRefGoogle Scholar
  30. Narayanan R, Liu B, Choudhary A (2009) Sentiment analysis of conditional sentences. In: Proceedings of the 2009 conference on empirical methods in natural language processing, vol 1. pp 180–189Google Scholar
  31. Nassirtoussi AK, Aghabozorgi S, Wah TY, Ngo DC (2015) Text mining of news-headlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Syst Appl 42(1):306–324CrossRefGoogle Scholar
  32. Nasukawa T, Yi J (2003) Sentiment analysis: capturing favorability using natural language processing. In: Proceedings of the 2nd international conference on knowledge capture, pp 70–77Google Scholar
  33. Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. InLREc 10:1320–1326Google Scholar
  34. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1-2):1–135CrossRefGoogle Scholar
  35. Park SC (2014) Competition and innovation for smart and creative society (CISCS). AI Soc 29(3):283CrossRefGoogle Scholar
  36. Poria S, Cambria E, Winterstein G, Huang GB (2014) Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl-Based Syst 69:45–63CrossRefGoogle Scholar
  37. Raj S, Kajla T (2015) Sentiment analysis of Swachh Bharat Abhiyan. Business analyt intel 3(1):32Google Scholar
  38. Ravi K, Ravi V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl-Based Syst 30(89):14–46CrossRefGoogle Scholar
  39. Sabat A (2015) Corporate social responsibility and Swachh Bharat Abhiyan. J Arts Humanit Manag 9(2):71–84Google Scholar
  40. Saxena VN (2015) A unique model recommended to be implemented in religious shrines across India as a part of ‘Swachh Bharat Abhiyan’. Int J Technol Innov Res 16:1–4Google Scholar
  41. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon based methods for sentiment analysis. Comput Linguist 37(2):267–307CrossRefGoogle Scholar
  42. Taddy M (2013) Measuring political sentiment on Twitter: factor optimal design for multinomial inverse regression. Technometrics 55(4):415–425MathSciNetCrossRefGoogle Scholar
  43. Tayal DK, Yadav S, Gupta K, Rajput B, Kumari K (2014) Polarity detection of sarcastic political tweets. In: 2014 International conference on computing for sustainable global development (INDIACom), 5 Mar 2014. IEEE, pp 625–628Google Scholar
  44. Tayal DK, Jain A, Arora S, Agarwal S, Gupta T, Tyagi N (2015) Crime detection and criminal identification in India using data mining techniques. AI Soc 30(1):117–127CrossRefGoogle Scholar
  45. Turney PD (2002) Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics, pp 417–424Google Scholar
  46. Tumasjan A et al. (2010) Predicting elections with twitter: what 140 characters reveal about political sentiment. ICWSM 10: 178–185Google Scholar
  47. Van de Camp M, Van den Bosch A (2012) The socialist network. Decis Support Syst 53(4):761–769CrossRefGoogle Scholar
  48. Vishwakarma D (2016) Swachh Bharat Abhiyan Clean India Abhiyan. Int Res J Manag IT Soc Sci 3(3):110–122Google Scholar
  49. Xuan HNT, Le AC, Nguyen LM (2012) Linguistic features for subjectivity classification. In: 2012 International conference on Asian language processing (IALP), 13 Nov 2012. IEEE, pp 17–20 Google Scholar
  50. Yu Y, Duan W, Cao Q (2013) The impact of social and conventional media on firm equity value: a sentiment analysis approach. Decis Support Syst 55(4):919–926CrossRefGoogle Scholar
  51. Zhang K, Xie Y, Yang Y, Sun A, Liu H, Choudhary A (2014) Incorporating conditional random fields and active learning to improve sentiment identification. Neural Netw 58:60–67CrossRefGoogle Scholar
  52. Zheng X, Lin Z, Wang X, Lin KJ, Song M (2014) Incorporating appraisal expression patterns into topic modeling for aspect and sentiment word identification. Knowl-Based Syst 61:29–47CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  1. 1.IGDTUWDelhiIndia
  2. 2.USET, GGSIPUDelhiIndia

Personalised recommendations