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Encyclopedia of Database Systems pp 4061–4063Cite as

Text Analytics in Social Media

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Synonyms

Entity extraction in social media; Opinion mining in social media; Text mining in social media

Definition

Text analytics in social media is the process of mining large amounts of user-generated texts in various forms including comments, status updates, and posts. The goal of text analytics in social media is to understand the aggregated opinion of online users in several domains including product reviews, news articles, and personal posts.

Text analytics in social media is also used to derive correlations between user demographics and their health and nutrition or demographics and political opinions. This task helps conduct large-scale population studies and design appropriate content recommendation and information dissemination policies.

Historical Background

Text analytics in social media relies on a number of tools and techniquesand is encountered in different application domains. Tools range from machine learning and natural language processing to clustering and...

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References

  1. Abbar S, Amer-Yahia S, Indyk P, Mahabadi S. Real-time recommendation of diverse related articles. In: Proceedings of the 22nd International World Wide Web Conference; 2013. p. 1–12.

    Google Scholar 

  2. Aramaki E, Maskawa S, Morita M. Twitter catches the flu: detecting influenza epidemics using Twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing; 2011. p. 1568–76.

    Google Scholar 

  3. Balasubramanyan R, Cohen WW, Pierce D, Redlawsk DP. Modeling polarizing topics: when do different political communities respond differently to the same news? In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media; 2012.

    Google Scholar 

  4. Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3(4–5):993–1022.

    MATH  Google Scholar 

  5. Calais Guerra PH, Meira Jr W, Cardie C. Sentiment analysis on evolving social streams: how self-report imbalances can help. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining; 2014. p. 443–52.

    Google Scholar 

  6. Castillo C, El-Haddad M, Pfeffer J, Stempeck M. Characterizing the life cycle of online news stories using social media reactions. In: Proceedings of the 2014 ACM Conference on Computer Supported Cooperative Work and Social Computing; 2014. p. 211–23.

    Google Scholar 

  7. Culotta A. Lightweight methods to estimate influenza rates and alcohol sales volume from twitter messages. Lang Resour Eval. 2013;47(1):217–238.

    Article  Google Scholar 

  8. Dang-Xuan L, Stieglitz S. Impact and diffusion of sentiment in political communication – an empirical analysis of political weblogs. In: Proceedings of the 6th International Conference on Weblogs and Social Media; 2012.

    Google Scholar 

  9. Dredze M. How social media will change public health. IEEE Intell Syst. 2012;27(4):81–84.

    Article  Google Scholar 

  10. Imran M, Castillo C, Lucas J, Meier P, Vieweg S. AIDR: artificial intelligence for disaster response. In: Proceedings of the 23rd International World Wide Web Conference (Companion Volume); 2014. p. 159–62.

    Google Scholar 

  11. Imran M, Elbassuoni S, Castillo C, Diaz F, Meier P. Practical extraction of disaster-relevant information from social media. In: Proceedings of the 22nd International World Wide Web Conference (Companion Volume); 2013. p. 1021–24.

    Google Scholar 

  12. Kwak H, Lee C, Park H, Moon SB. What is Twitter, a social network or a news media? In: Proceedings of the 19th International World Wide Web Conference; 2010. p. 591–600.

    Google Scholar 

  13. Meier P. Next generation humanitarian computing. In: Proceedings of the 2014 ACM Conference on Computer Supported Cooperative Work and Social Computing; 2014. p. 1573.

    Google Scholar 

  14. Park M, McDonald DW, Cha M. Perception differences between the depressed and non-depressed users in Twitter. In: Proceedings of the 7th International Conference on Weblogs and Social Media; 2013.

    Google Scholar 

  15. Paul MJ, Dredze M. You are what you tweet: analyzing Twitter for public health. In: Proceedings of the 5th International Conference on Weblogs and Social Media; 2011.

    Google Scholar 

  16. Teodoro R, Naaman M. Fitter with Twitter: understanding personal health and fitness activity in social media. In: Proceedings of the 7th International Conference on Weblogs and Social Media; 2013.

    Google Scholar 

  17. Thelwall M, Buckley K. Topic-based sentiment analysis for the social Web: the role of mood and issue-related words. J Am Soc Inf Sci Technol. 2013;64(8):1608–17.

    Article  Google Scholar 

  18. Thelwall M, Buckley K, Paltoglou G, Cai D, Kappas A. Sentiment strength detection in short informal text. J Am Soc Inf Sci Technol. 2010;61(12):2544–58.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. CNRS, Univ. Grenoble Alps, Grenoble, France

    Sihem Amer-Yahia

  2. Laboratoire d’Informatique de Grenoble, CNRS-LIG, Saint Martin-d’Hères, Grenoble, France

    Sihem Amer-Yahia

  3. Qatar Computing Research Institute, Doha, Qatar

    Sofiane Abbar

  4. Grenoble Informatics Laboratory (LIG), Grenoble, France

    Noha Ibrahim

Authors
  1. Sihem Amer-Yahia
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  2. Sofiane Abbar
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  3. Noha Ibrahim
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Corresponding author

Correspondence to Sihem Amer-Yahia .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

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Amer-Yahia, S., Abbar, S., Ibrahim, N. (2018). Text Analytics in Social Media. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_80672

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