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The Multifaceted Concept of Context in Sentiment Analysis

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Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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References

  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. Kumar, A., Garg, G.: Sentiment analysis of multimodal twitter data. Multimedia Tools Appl., 1–17 (2019)

    Google Scholar 

  3. Bouazizi, M., Ohtsuki, T.O.: A pattern-based approach for sarcasm detection on Twitter. IEEE Access 4, 5477–5488 (2016)

    Article  Google Scholar 

  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. 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. Han, H., Bai, X., Li, P.: Neural Comput. Appl. (2018). https://doi.org/10.1007/s00521-018-3698-4

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Feng, S., Wang, Y., Liu, L., et al.: World Wide Web (2018). https://doi.org/10.1007/s11280-018-0529-6

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  13. Muhammad, A., Wiratunga, N., Lothian, R.: Contextual sentiment analysis for social media genres. Knowl.Based Syst. 108, 92–101 (2016)

    Article  Google Scholar 

  14. Saif, H., He, Y., Fernandez, M., Alani, H.: Contextual semantics for sentiment analysis of Twitter. Inf. Process. Manage. 52(1), 5–19 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  17. Wu, F., Huang, Y., Song, Y.: Structured microblog sentiment classification via social context regularization. Neurocomputing 175, 599–609 (2016)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University, Delhi, India

    Akshi Kumar & Geetanjali Garg

Authors
  1. Akshi Kumar
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  2. Geetanjali Garg
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Corresponding author

Correspondence to Geetanjali Garg .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  2. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Rangpo, India

    Akash Kumar Bhoi

  4. Division of Information and Communication, Baekseok University, Cheonan-si, Ch’ungch’ong-namdo, Korea (Republic of)

    Gyoo-Soo Chae

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Kumar, A., Garg, G. (2020). The Multifaceted Concept of Context in Sentiment Analysis. In: Mallick, P., Balas, V., Bhoi, A., Chae, GS. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1040. Springer, Singapore. https://doi.org/10.1007/978-981-15-1451-7_44

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