Encyclopedia of Social Network Analysis and Mining

2018 Edition
| Editors: Reda Alhajj, Jon Rokne

Twitter Microblog Sentiment Analysis

  • Guangxia Li
  • Zhen Hai
  • Peilin Zhao
  • Kuiyu Chang
  • Steven C. H. Hoi
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-7131-2_265

Synonyms

Glossary

Sentiment Analysis

The automatic analysis of opinions, sentiments, and subjectivity in text. It aims to determine the sentiment associated with a topic or context

Online Learning

Online learning algorithms update the learning model incrementally whenever they receive new data. They are usually highly efficient and scalable

Multitask Learning

The problem of jointly solving several related machine learning tasks by leveraging the commonality among tasks

Definition

Twitter microblog sentiment analysis aims to identify and detect the sentiments or emotions present in a microblog post. The techniques developed for microblog sentiment analysis can also be applied to classify social media data in a real-time manner.

Introduction

Microblogs, such as Twitter and Facebook status updates, allow users to publish short snippets of text online. Compared to blogs, microblogs are typically shorter in length but updated much more...

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

References

  1. Barbosa L, Feng J (2010) Robust sentiment detection on twitter from biased and noisy data. In: COLING 2010, 23rd international conference on computational linguistics, Posters volume, 23–27 Aug 2010, Beijing, pp 36–44Google Scholar
  2. Bifet A, Frank E (2010) Sentiment knowledge discovery in twitter streaming data. In: Discovery science – 13th international conference, DS 2010, Canberra, 6–8 Oct 2010. Proceedings, pp 1–15Google Scholar
  3. Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585MathSciNetMATHGoogle Scholar
  4. Davidov D, Tsur O, Rappoport A (2010a) Enhanced sentiment learning using twitter hashtags and smileys. In: COLING 2010, 23rd international conference on computational linguistics, Posters volume, 23–27 Aug 2010, Beijing, pp 241–249. URL http://aclweb.org/anthology-new/C/C10/C10-2028.pdf
  5. Davidov D, Tsur O, Rappoport A (2010b) Semi-supervised recognition of sarcasm in twitter and amazon. In: Proceedings of the fourteenth conference on computational natural language learning, CoNLL 2010, Uppsala, 15–16 July 2010, pp 107–116. URL http://aclweb.org/anthology/W/W10/W10-2914.pdf
  6. dos Santos CN, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: COLING 2014, 25th international conference on computational linguistics, proceedings of the conference: technical papers, 23–29 August 2014, Dublin, pp 69–78. URL http://aclweb.org/anthology/C/C14/C14-1008.pdf
  7. Java A, Song X, Finin T, Tseng BL (2007) Why we twitter: an analysis of a microblogging community. In: Advances in web mining and web usage analysis, 9th international workshop on knowledge discovery on the web, WebKDD 2007, and 1st international workshop on social networks analysis, SNA-KDD 2007, San Jose, 12–15 Aug 2007. Revised papers, pp 118–138Google Scholar
  8. Kouloumpis E, Wilson T, Moore JD (2011) Twitter sentiment analysis: the good the bad and the omg! In: Proceedings of the fifth international conference on weblogs and social media, Barcelona, Catalonia, 17–21 July 2011. URL http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2857
  9. Li G, Chang K, Hoi SCH, Liu W, Jain R (2011) Collaborative online learning of user generated content. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, 24–28 Oct 2011, pp 285–290.Google Scholar
  10. Li G, Hoi SCH, Chang K, Liu W, Jain R (2014) Collaborative online multitask learning. IEEE Trans Knowl Data Eng 26(8):1866–1876.  https://doi.org/10.1109/TKDE.2013.139CrossRefGoogle Scholar
  11. Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the international conference on language resources and evaluation, LREC 2010, 17–23 May 2010, Valletta. URL http://www.lrec-conf.org/proceedings/lrec2010/summaries/385.html
  12. Pang B, Lee L (2007) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRefGoogle Scholar
  13. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. CoRR cs.CL/0205070. URL http://arxiv.org/abs/cs.CL/0205070
  14. Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B (2014) Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd annual meeting of the association for computational linguistics, ACL 2014, 22–27 June 2014, Baltimore, vol 1: long papers, pp 1555–1565Google Scholar
  15. Yang P, Li G, Zhao P, Li X, Gollapalli SD (2016) Learning correlative and personalized structure for online multi-task classification. In: Proceedings of the 2016 SIAM international conference on data mining, Miami, 5–7 May 2016, pp 666–674.  https://doi.org/10.1137/1.9781611974348.75

Copyright information

© Springer Science+Business Media LLC, part of Springer Nature 2018

Authors and Affiliations

  • Guangxia Li
    • 1
  • Zhen Hai
    • 2
  • Peilin Zhao
    • 3
  • Kuiyu Chang
    • 4
  • Steven C. H. Hoi
    • 5
  1. 1.School of Computer Science and TechnologyXidian UniversityXi’anChina
  2. 2.Institute for Infocomm ResearchSingaporeSingapore
  3. 3.Ant FinancialHangzhouChina
  4. 4.LinkSure China HoldingSingaporeSingapore
  5. 5.School of Information SystemsSingapore Management UniversitySingaporeSingapore

Section editors and affiliations

  • Przemysław Kazienko
    • 1
  • Jaroslaw Jankowski
    • 2
  1. 1.Department of Computer Science and Management, Institute of InformaticsWrocław University of TechnologyWrocławPoland
  2. 2.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland