Skip to main content
SpringerLink
Log in

Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments

  • Conference paper
  • First Online:
Computational Social Networks (CSoNet 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9795))

Included in the following conference series:

Abstract

The most of the people have their account on social networks (e.g. Facebook, Vkontakte) where they express their attitude to different situations and events. Facebook provides only the positive mark as a like button and share. However, it is important to know the position of a certain user on posts even though the opinion is negative. Positive, negative and neutral attitude can be extracted from the comments of users. Overall information about positive, negative and neutral opinion can bring understanding how people react in a position. Moreover, it is important to know how attitude is changing during the time period. The contribution of the paper is a new method based on sentiment text analysis for detection and prediction negative and positive patterns for Facebook comments which combines (i) real-time sentiment text analysis for pattern discovery and (ii) batch data processing for creating opinion forecasting algorithm. To perform forecast we propose two-steps algorithm where: (i) patterns are clustered using unsupervised clustering techniques and (ii) trend prediction is performed based on finding the nearest pattern from the certain cluster. Case studies show the efficiency and accuracy (Avg. MAE = 0.008) of the proposed method and its practical applicability. Also, we discovered three types of users attitude patterns and described them.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The title of the post: Obama bans solitary confinement for juveniles and low-level offenders, https://www.facebook.com/bbcnews/posts/10153348871732217.

References

  1. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, LSM 2011, pp. 30–38. Association for Computational Linguistics, Stroudsburg (2011)

    Google Scholar 

  2. Bermingham, A., Smeaton, A.: Classifying sentiment in microblogs: is brevity an advantage is brevity an advantage? In: 19th ACM International Conference on Information and Knowledge Management, pp. 1833–1836. ACM (2010)

    Google Scholar 

  3. Cheng, O.K.M., Lau, R.: Big data stream analytics for near real-time sentiment analysis. J. Comput. Commun. 3, 189–195 (2015)

    Article  Google Scholar 

  4. Choy, M., Cheong, M.L.F., Laik, M.N., Shung, K.P.: A sentiment analysis of Singapore Presidential Election 2011 using Twitter data with census correction. arXiv:1108.5520 (2011)

  5. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)

    Article  Google Scholar 

  6. Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10(4), 255–268 (2006)

    Article  Google Scholar 

  7. Gamon, M.: Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis. In: 20th International Conference on Computational Linguistics, p. 841-es. Association for Computational Linguistics, Stroudsburg (2004)

    Google Scholar 

  8. Godbole, N., Srinivasaiah, M., Skiena, S.: Large-scale sentiment analysis for news and blogs. In: ICWSM, vol. 7, no. 21, pp. 219–222 (2007)

    Google Scholar 

  9. Golubev, A., Chechetkin, I., Solnushkin, K.S., Sadovnikova, N., Parygin, D., Shcherbakov, M.V.: Strategway: web solutions for building public transportation routes using big geodata analysis. In: 17th International Conference on Information Integration and Web-Based Applications and Services (iiWAS 2015), pp. 91:1–91:4. ACM, New York (2015)

    Google Scholar 

  10. Grömping, M.: Echo chambers: partisan facebook groups during the 2014 Thai election. Asia Pacific Media Educ. 24, 39–59 (2014)

    Article  Google Scholar 

  11. Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: 35th ACL and 8th EACL, pp. 174–181. ACL, Somerset (1997)

    Google Scholar 

  12. Honeycutt, C., Herring, S.C.: Beyond microblogging: conversation and collaboration via twitter. In: 42nd Hawaii International Conference on System Sciences, pp. 1–10 (2009)

    Google Scholar 

  13. Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: tweets as electronic word of mouth. J. Am. Soc. Inform. Sci. Technol. 60, 1–20 (2009)

    Article  Google Scholar 

  14. Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: 19th International Conference on World Wide Web, pp. 591–600 (2010)

    Google Scholar 

  15. Lau, R.Y.K., Xia, Y., Ye, Y.: A probabilistic generative model for mining cybercriminal networks from online social media. IEEE Comput. Intell. Mag. 9, 31–43 (2014)

    Article  Google Scholar 

  16. Natural Language Toolkit. http://www.nltk.org/

  17. Nguyen, V.H., Nguyen, H.T., Snasel, V.: Normalization of Vietnamese tweets on twitter. In: Abraham, A., Jiang, X.H., Snášel, V., Pan, J.-S. (eds.) ECC 2015. AISC, vol. 370, pp. 179–189. Springer International Publishing, Cham (2015)

    Chapter  Google Scholar 

  18. Nguyen, H.T., Duong, P.H., Le, T.Q.: A multifaceted approach to sentence similarity. In: Huynh, V.N., Inuiguchi, M., Demoeux, T. (eds.) IUKM 2015. LNCS, vol. 9376, pp. 303–314. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25135-6_29

    Chapter  Google Scholar 

  19. Pang, B., Lee, L., Vaithyanathan, D.: Thumbs up? Sentiment classification using machine learning techniques. In: ACL-02 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), vol. 10, pp. 79–86. Association for Computational Linguistics, Stroudsburg (2002)

    Google Scholar 

  20. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2, 1–135 (2008)

    Article  Google Scholar 

  21. The Top 20 Valuable Facebook Statistics, December 2015. https://zephoria.com/top-15-valuable-facebook-statistics/

  22. Trinh, S., Nguyen, L., Vo, M., Do, P.: Lexicon-based sentiment analysis of facebook comments in Vietnamese language. In: Król, D., Madeyski, L., Nguyen, N.T. (eds.) Recent Developments in Intelligent Information and Database Systems. SCI, vol. 642, pp. 263–276. Springer, Cham (2016)

    Chapter  Google Scholar 

  23. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Fourth International AAAI Conference on Weblogs and Social Media, pp. 178–185. AAAI (2010)

    Google Scholar 

  24. Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: 40th Annual Meeting of the ACL (ACL 2002), pp. 417–424. ACL, Philadelphia (2002)

    Google Scholar 

  25. Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 U.S. presidential election cycle. In: The ACL 2012 System Demonstrations, pp. 115–120. Association for Computational Linguistics, Stroudsburg (2012)

    Google Scholar 

  26. Weinstein, C., Campbell, W.M., Delaney, B.W., OLeary, G.: Modeling and detection techniques for counter-terror social network analysis and intent recognition. In IEEE Aerospace Conference, pp. 1–16. IEEE Press (2009)

    Google Scholar 

  27. Williams, C., Gulati, G.: What is a social network worth? Facebook and vote share in the 2008 presidential primaries. In: Annual Meeting of the American Political Science Association, Boston, MA, pp. 1–17 (2008)

    Google Scholar 

  28. Zhang, Y., Dang, Y., Chen, H., Thurmond, M., Larson, C.: Automatic online news monitoring and classification for syndromic surveillance. Decis. Support Syst. 47(4), 508–517 (2009)

    Article  Google Scholar 

Download references

Acknowledgments

The reported study was partially supported by RFBR, research project No. 16-37-60066 and research project MD-6964.2016.9.

Author information

Authors and Affiliations

  1. Computer Aided Design Department, Volgograd State Technical Univeristy, Lenin Avenue 28, 400005, Volgograd, Russia

    Hieu Tran & Maxim Shcherbakov

Authors
  1. Hieu Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maxim Shcherbakov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hieu Tran .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University , Ho Chi Minh City, Vietnam

    Hien T. Nguyen

  2. VSB-Technical University of Ostrava , Ostrava, Poland

    Vaclav Snasel

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Tran, H., Shcherbakov, M. (2016). Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42345-6_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42344-9

  • Online ISBN: 978-3-319-42345-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation