Encyclopedia of Database Systems

Living Edition
| Editors: Ling Liu, M. Tamer Özsu

Text Analytics in Social Media

  • Sihem Amer-YahiaEmail author
  • Sofiane Abbar
  • Noha Ibrahim
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7993-3_80672-1
  • 48 Downloads

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...

Keywords

Pattern Recognition Communication Network Data Mining Social Medium Computer Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Recommended Reading

  1. 1.
    Abbar S, Amer-Yahia S, Indyk P, Mahabadi S. Real-time recommendation of diverse related articles. In: WWW; 2013. p. 1–12.zbMATHGoogle Scholar
  2. 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, ACL; 2011. p. 1568–76.Google Scholar
  3. 3.
    Balasubramanyan R, Cohen WW, Pierce D, Redlawsk DP. Modeling polarizing topics: when do different political communities respond differently to the same news? In: ICWSM; 2012.Google Scholar
  4. 4.
    Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.zbMATHGoogle Scholar
  5. 5.
    Calais Guerra PH, Meira Jr W, Cardie C. Sentiment analysis on evolving social streams: how self-report imbalances can help. In: WSDM; 2014. p. 443–52.Google Scholar
  6. 6.
    Castillo C, El-Haddad M, Pfeffer J, Stempeck M. Characterizing the life cycle of online news stories using social media reactions. In: CSCW; 2014. p. 211–23.Google Scholar
  7. 7.
    Culotta A. Lightweight methods to estimate influenza rates and alcohol sales volume from twitter messages. Lang Resour Eval. 2013;1–22.Google Scholar
  8. 8.
    Dang-Xuan L, Stieglitz S. Impact and diffusion of sentiment in political communication – an empirical analysis of political weblogs. In: ICWSM; 2012.Google Scholar
  9. 9.
    Dredze M. How social media will change public health. IEEE Intell Syst. 2012;27(4):81–84.CrossRefGoogle Scholar
  10. 10.
    Imran M, Castillo C, Lucas J, Meier P, Vieweg S. AIDR: artificial intelligence for disaster response. In: WWW (Companion Volume); 2014. p. 159–62.Google Scholar
  11. 11.
    Imran M, Elbassuoni S, Castillo C, Diaz F, Meier P. Practical extraction of disaster-relevant information from social media. In: WWW (Companion Volume); 2013. p. 1021–24.Google Scholar
  12. 12.
    Kwak H, Lee C, Park H, Moon SB. What is Twitter, a social network or a news media? In: WWW; 2010. p. 591–600.Google Scholar
  13. 13.
    Meier P. Next generation humanitarian computing. In: CSCW; 2014. p. 1573.Google Scholar
  14. 14.
    Park M, McDonald DW, Cha M. Perception differences between the depressed and non-depressed users in Twitter. In: ICWSM; 2013.Google Scholar
  15. 15.
    Paul MJ, Dredze M. You are what you tweet: analyzing Twitter for public health. In: ICWSM; 2011.Google Scholar
  16. 16.
    Teodoro R, Naaman M. Fitter with Twitter: understanding personal health and fitness activity in social media. In: ICWSM; 2013.Google Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Laboratoire d’Informatique de GrenobleCNRS and LIGGrenobleFrance
  2. 2.Qatar Computing Research InstituteDohaQatar
  3. 3.Grenoble Inforamtics Laboratory (LIG)GrenobleFrance

Section editors and affiliations

  • Fatma Özcan
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA