Advertisement

The Multifaceted Concept of Context in Sentiment Analysis

  • Akshi Kumar
  • Geetanjali GargEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)

Abstract

The contemporary web is about communication, collaboration, participation, and sharing. Currently, the sharing of content on the web ranges from sharing of ideas and information which includes text, photos, videos, audios, and memes to even gifs. Moreover, the language and linguistic tone of user-generated content are informal and indistinct. Analyzing explicit and clear sentiment is challenging owing to language constructs which may intensify or flip the polarity within the posts. Context-based sentiment analysis is the domain of study which deals with comprehending cues which can enhance the prediction accuracy of the generic sentiment analysis as well as facilitate fine grain analysis of varied linguistic constructs such as sarcasm, humor, or irony. This work is preliminary to understand the what, how and why of using the context in sentiment analysis. The concept of ‘context in use’ is described by exemplifying the types of context. A strength–weakness–opportunity–threat (SWOT) matrix is made to demonstrate the effectiveness of context-based sentiment analysis.

Keywords

Sentiment analysis Context Social media SWOT 

References

  1. 1.
    Kumar, A., Garg, G.: Systematic literature review on context-based sentiment analysis in social multimedia. Multimedia Tools Appl., 1–32 (2019)Google Scholar
  2. 2.
    Kumar, A., Garg, G.: Sentiment analysis of multimodal twitter data. Multimedia Tools Appl., 1–17 (2019)Google Scholar
  3. 3.
    Bouazizi, M., Ohtsuki, T.O.: A pattern-based approach for sarcasm detection on Twitter. IEEE Access 4, 5477–5488 (2016)CrossRefGoogle Scholar
  4. 4.
    Bamman, D., Smith, N.A.: Contextualized sarcasm detection on twitter. In: Ninth International AAAI Conference on Web and Social Media (2015)Google Scholar
  5. 5.
    Wang, Z., Wu, Z., Wang, R., Ren, Y.: Twitter sarcasm detection exploiting a context-based model. In: International Conference on Web Information Systems Engineering, pp. 77–91. Springer, Cham (2015)Google Scholar
  6. 6.
    Han, H., Bai, X., Li, P.: Neural Comput. Appl. (2018).  https://doi.org/10.1007/s00521-018-3698-4CrossRefGoogle Scholar
  7. 7.
    Majumder, N., Hazarika, D., Gelbukh, A., Cambria, E., Poria, S.: Multimodal sentiment analysis using hierarchical fusion with context modeling. Knowl. Based Syst. 161, 124–133 (2018)CrossRefGoogle Scholar
  8. 8.
    Sheik, R., Philip, S.S., Sajeev, A., Sreenivasan, S., Jose, G.: Entity level contextual sentiment detection of topic sensitive influential Twitterers using SentiCircles. Data Eng. Intell. Comput. 207–216. Springer, Singapore (2018)Google Scholar
  9. 9.
    Feng, S., Wang, Y., Liu, L., et al.: World Wide Web (2018).  https://doi.org/10.1007/s11280-018-0529-6CrossRefGoogle Scholar
  10. 10.
    Deng, S., Sinha, A.P., Zhao, H.: Resolving ambiguity in sentiment classification: the role of dependency features. ACM Trans. Manage. Inf. Syst. (TMIS) 8(2–3), 4 (2017)Google Scholar
  11. 11.
    Jiménez-Zafra, S.M., Montejo-Ráez, A., Martin, M., Lopez, L.A.U.: SINAI at SemEval-2017 Task 4: user based classification.. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 634–639 (2017)Google Scholar
  12. 12.
    Fersini, E., Pozzi, P.A., Messina, E.: Approval network: a novel approach for sentiment analysis in social networks. World Wide Web 20(4), 831–854 (2017)CrossRefGoogle Scholar
  13. 13.
    Muhammad, A., Wiratunga, N., Lothian, R.: Contextual sentiment analysis for social media genres. Knowl.Based Syst. 108, 92–101 (2016)CrossRefGoogle Scholar
  14. 14.
    Saif, H., He, Y., Fernandez, M., Alani, H.: Contextual semantics for sentiment analysis of Twitter. Inf. Process. Manage. 52(1), 5–19 (2016)CrossRefGoogle Scholar
  15. 15.
    Meire, M., Ballings, M., Van den Poel, D.: The added value of auxiliary data in sentiment analysis of Facebook posts. Decis. Support Syst. 89, 98–112 (2016)CrossRefGoogle Scholar
  16. 16.
    Nakov, P., Rosenthal, S., Kiritchenko, S., Mohammad, S.M., Kozareva, Z., Ritter, A., Stoyanov, V., Zhu, X.: Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts. Lang. Resour. Eval. 50(1), 35–65 (2016)CrossRefGoogle Scholar
  17. 17.
    Wu, F., Huang, Y., Song, Y.: Structured microblog sentiment classification via social context regularization. Neurocomputing 175, 599–609 (2016)CrossRefGoogle Scholar
  18. 18.
    Hridoy, S.A.A., Ekram, M.T., Islam, M.S., Ahmed, F., Rahman, R.M.: Localized twitter opinion mining using sentiment analysis. Decis. Anal. 2(1), 8 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringDelhi Technological UniversityDelhiIndia

Personalised recommendations