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Smart Innovations in Communication and Computational Sciences pp 393–400Cite as

TWEESENT: A Web Application on Sentiment Analysis

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 851))

Abstract

Twitter, being one of the popular social media site, is a viable source of data for extracting and analyzing the sentiment of the users. User sentiment varies with their demographic attributes and social background; which results in different sentiment among users for the same event or topic. In this paper an emotion/polarity detection method is proposed, to detect sentiment state of the twitter users. The proposed method is implemented as a web application called “TweeSent”. TweeSent classifies the tweets in terms of emotions and polarity across demographic attributes of the users such as location, age, gender, and occupation class. The demographic attributes of the tweets are estimated by using domain depend dictionaries. In order to evaluate the performance of our system, the tweets on demonetization in India is extracted, analyzed and compared with the government survey reports.

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References

  1. Jurek, A., Mulvenna, M.D., Bi, Y.: Improved lexicon-based sentiment analysis for social media analytics. Secur. Inform. 4(1), 9 (2015)

    Article  Google Scholar 

  2. Andreevskaia, A., Bergler, S., Urseanu, M.: All blogs are not made equal: exploring genre differences in sentiment tagging of blogs. In: ICWSM, Mar 2007

    Google Scholar 

  3. Aman, S., Szpakowicz, S.: Identifying expressions of emotion in text. In: Text, Speech and Dialogue, pp. 196–205. Springer, Berlin, Heidelberg (2007)

    Google Scholar 

  4. Bollen, J., Mao, H., Pepe, A.: Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: ICWSM, vol. 11, pp. 450–453 (2011)

    Google Scholar 

  5. Kundi, F.M., Khan, A., Ahmad, S., Asghar, M.Z.: Lexicon-based sentiment analysis in the social web. J. Basic Appl. Sci. Res. 4(6), 238–248 (2014)

    Google Scholar 

  6. Westling, A., Brynielsson, J., Gustavi, T.: Mining the web for sympathy: the pussy riot case. In: 2014 IEEE Joint Intelligence and Security Informatics Conference (JISIC), pp. 123–128. IEEE, Sept 2014

    Google Scholar 

  7. Mislove, A., Lehmann, S., Ahn, Y.Y., Onnela, J.P., Rosenquist, J.N.: Understanding the demographics of Twitter users. In: 5th ICWSM, vol. 11, (2011)

    Google Scholar 

  8. Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, pp. 37–44. ACM, Oct 2010

    Google Scholar 

  9. Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M.: Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLoS ONE 8(9), e73791 (2013). https://doi.org/10.1371/journal.pone.0073791. PMID: 24086296

    Article  Google Scholar 

  10. Ludu, P.S.: Inferring latent attributes of an Indian Twitter user using celebrities and class influencers. In: Proceedings of the 1st ACM Workshop on Social Media World Sensors, pp. 9–15. ACM, Sept 2015

    Google Scholar 

  11. Sloan, L., Morgan, J., Burnap, P., Williams, M.: Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PLoS One 10(3), e0115545 (2015)

    Article  Google Scholar 

  12. Bamman, D., Eisenstein, J., Schnoebelen, T.: Gender identity and lexical variation in social media. J. Socioling. 18(2), 135–160 (2014)

    Article  Google Scholar 

  13. Mandel, B., Culotta, A., Boulahanis, J., Stark, D., Lewis, B., Rodrigue, J.: A demographic analysis of online sentiment during hurricane irene. In: Proceedings of the Second Workshop on Language in Social Media, pp. 27–36. Association for Computational Linguistics, June 2012

    Google Scholar 

  14. Oktay, H., Firat, A., Ertem, Z.: Demographic Breakdown of Twitter Users: An Analysis Based on Names. Academy of Science and Engineering (ASE) (2014)

    Google Scholar 

  15. Nguyen, D.P., Gravel, R., Trieschnigg, R.B., Meder, T.: “How old do you think I am?” A study of language and age in Twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, ICWSM 2013, 08–10 July 2013, Cambridge, MA, USA, pp. 439-448. AAAI Press. ISBN 978-1-57735-610-3

    Google Scholar 

  16. Siswanto, E., Khodra, M.L.: Predicting latent attributes of Twitter user by employing lexical features. In: 2013 International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 176–180. IEEE, Oct 2013

    Google Scholar 

  17. Bi, Y., Li, M., Leow, D., Huang, R.: Political Sentiment Visualization Data Analysis and Visualization using Voxgov US Federal Government Media Releases (2014)

    Google Scholar 

  18. Mohammad, S.M., Turney, P.D.: NRC emotion lexicon. NRC Technical Report (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, New Delhi, Delhi, India

    Sweta Swain & K. R. Seeja

Authors
  1. Sweta Swain
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  2. K. R. Seeja
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Corresponding author

Correspondence to K. R. Seeja .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering Department, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  2. Department of Information Technology, Rajkiya Engineering College, Azamgarh, Uttar Pradesh, India

    Munesh C. Trivedi

  3. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  4. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    A. K. Misra

  5. Department of Computer Science and Engineering, University of Mauritius, Moka, Mauritius

    Khedo Kavi Kumar

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Swain, S., Seeja, K.R. (2019). TWEESENT: A Web Application on Sentiment Analysis. In: Tiwari, S., Trivedi, M., Mishra, K., Misra, A., Kumar, K. (eds) Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 851. Springer, Singapore. https://doi.org/10.1007/978-981-13-2414-7_36

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