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Extracting Emotion and Sentiment Quotient of Viral Information Over Twitter

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Intelligent Systems Design and Applications (ISDA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 418))

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Abstract

In social media platforms, viral or trending information are consumed for several decision-making, as they harness the information flux. In apt to this, millions of real-time users often consumed the data co-located to these virilities. Thus, encompass sentiment and co-located emotions, could be utilized for the analysis and decision support. Traditionally, sentiment tool offers limited insights and lacks in the extraction of emotional impact. In these settings, estimation of emotion quotient becomes a multifaceted task. The proposed novel algorithm aims, to (i) extract the sentiment and co-located emotions quotient of viral information and (ii) utilities for comprehensive comparison on co-occurring viral information, and sentiment analysis over Twitter data. The emotion and micro-sentiment reveals several valuable insight of a viral topic and assists in decision support. A use-case analysis over real-time extracted data asserts significant insights, as generated sentiments and emotional effects reveals co-relations caused by viral/trending information. The algorithm delivers an efficient, robust, and adaptable solution for the sentiment analysis also.

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References

  1. Bikel, D.M., Sorensen, J.: If we want your opinion. In: International Conference on Semantic Computing (ICSC 2007), pp. 493–500 (2007)

    Google Scholar 

  2. Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)

    Article  Google Scholar 

  3. Chen, R., Xu, W.: The determinants of online customer ratings: a combined domain ontology and topic text analytics approach. Electron. Commer. Res. 17, 31–50 (2016). https://doi.org/10.1007/s10660-016-9243-6

    Article  Google Scholar 

  4. Esuli, A., Sebastiani, F.: SentiWordNet: a publicly available lexical resource for opinion mining. In: Proceedings of 5th Language Resources and Evaluation, vol. 6, pp. 417–422 (2006)

    Google Scholar 

  5. Godbole, N., Srinivasaiah, M., Skiena, S.: Large-scale sentiment analysis for news and blogs. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM), vol. 7, no. 21, pp. 219–222 (2007)

    Google Scholar 

  6. Van de Kauter, M., Breesch, D., Hoste, V.: Fine-grained analysis of explicit and implicit sentiment in financial news articles. Expert Syst. Appl. 42(11), 4999–5010 (2015)

    Article  Google Scholar 

  7. Loper, E., Bird, S.: NLTK: the natural language toolkit. arXiv preprint arXiv:cs/0205028 (2002)

  8. Li, Y., Qin, Z., Xu, W., Guo, J.: A holistic model of mining product aspects and associated sentiments from online reviews. Multimedia Tools Appl. 74(23), 10177–10194 (2015)

    Article  Google Scholar 

  9. Liu, B.: Opinion mining and sentiment analysis. In: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, pp. 459–526 (2011). https://doi.org/10.1007/978-3-642-19460-3_11

  10. Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167 (2012)

    Google Scholar 

  11. Liu, P., Gulla, J.A., Zhang, L.: Dynamic topic-based sentiment analysis of large-scale online news. In: Cellary, W., Mokbel, M.F., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2016. LNCS, vol. 10042, pp. 3–18. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48743-4_1

    Chapter  Google Scholar 

  12. Ma, Y., Chen, G., Wei, Q.: Finding users preferences from large-scale online reviews for personalized recommendation. Electron. Commer. Res. 17(1), 3–29 (2017)

    Article  Google Scholar 

  13. Mo, S.Y.K., Liu, A., Yang, S.Y.: News sentiment to market impact and its feedback effect. Environ. Syst. Decis. 36(2), 158–166 (2016)

    Article  Google Scholar 

  14. Nassirtoussi, A.K., Aghabozorgi, S., Wah, T.Y., Ngo, D.C.L.: Text mining of newsheadlines for FOREX market prediction: a multi-layer dimension reduction algorithm with semantics and sentiment. Expert Syst. Appl. 42(1), 306–324 (2015)

    Article  Google Scholar 

  15. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008). https://doi.org/10.1561/1500000011

    Article  Google Scholar 

  16. Parkhe, V., Biswas, B.: Sentiment analysis of movie reviews: finding most important movie aspects using driving factors. Soft. Comput. 20(9), 3373–3379 (2016)

    Article  Google Scholar 

  17. Peng, J., Choo, K.K.R., Ashman, H.: Astroturfing detection in social media: using binary n-gram analysis for authorship attribution. In: Proceedings of the 15th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2016), pp. 121–1286 (2016)

    Google Scholar 

  18. Peng, J., Choo, K.K.R., Ashman, H.: Bit-level N-gram based forensic authorship analysis on social media: identifying individuals from linguistic profiles. J. Netw. Comput. Appl. 70, 171–182 (2016). https://doi.org/10.1016/j.jnca.2016.04.001

    Article  Google Scholar 

  19. Pröllochs, N., Feuerriegel, S., Neumann, D.: Enhancing sentiment analysis of financial news by detecting negation scopes. In: Proceedings of the 48th Hawaii International Conference on System Sciences (HICSS), pp. 959–968 (2015)

    Google Scholar 

  20. Rout, J., Dalmia, A., Choo, K.K.R., Bakshi, S., Jena, S.: Revisiting semi-supervised learning for online deceptive review detection. IEEE Access 5(1), 1319–1327 (2017)

    Article  Google Scholar 

  21. Rout, J., Singh, S., Jena, S., Bakshi, S.: Deceptive review detection using labeled and unlabeled data. Multimedia Tools Appl. 76(3), 3187–3211 (2017)

    Article  Google Scholar 

  22. Sadegh, M., Ibrahim, R., Othman, Z.A.: Opinion mining and sentiment analysis: a survey. Int. J. Comput. Technol. 2(3), 171–178 (2012)

    Article  Google Scholar 

  23. Song, L., Lau, R.Y.K., Kwok, R.C.W., Mirkovski, K., Dou, W.: Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection. Electron. Commer. Res. 17(1), 51–81 (2017)

    Article  Google Scholar 

  24. Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. Expert Syst. Appl. 36(7), 10760–10773 (2009)

    Article  Google Scholar 

  25. Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424 (2002)

    Google Scholar 

  26. Wang, D., Li, J., Xu, K., Wu, Y.: Sentiment community detection: exploring sentiments and relationships in social networks. Electron. Commer. Res. 17(1), 103–132 (2017)

    Article  Google Scholar 

  27. Zheng, L., Wang, H., Gao, S.: Sentimental feature selection for sentiment analysis of Chinese online reviews. Int. J. Mach. Learn. Cybern. 9, 75–84 (2015). https://doi.org/10.1007/s13042-015-0347-4

    Article  Google Scholar 

  28. Alves, A.L.F.: A spatial and temporal sentiment analysis approach applied to Twitter microtexts. J. Inf. Data Manag. 6, 118 (2015)

    Google Scholar 

  29. Chaabani, Y., Toujani, R., Akaichi, J.: Sentiment analysis method for tracking touristics reviews in social media network. In: Proceedings of the International Conference on Intelligent Interactive Multimedia Systems and Services, Australia, 20–22 May 2018

    Google Scholar 

  30. Contractor, D.: Tracking political elections on social media: applications and experience. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, 25–31 July 2015

    Google Scholar 

  31. Brynielsson, J., Johansson, F., Jonsson, C., Westling, A.: Emotion classification of social media posts for estimating people’s reactions to communicated alert messages during crises. Secur. Inform. 3(1), 1–11 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology, Surathkal, India

    Pawan Kumar & Reiben Eappen Reji

  2. National Institute of Technology, Kurukshetra, India

    Vikram Singh

Authors
  1. Pawan Kumar
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  2. Reiben Eappen Reji
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  3. Vikram Singh
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Corresponding author

Correspondence to Vikram Singh .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  3. Institut für Wirtschaftsinformatik, Fachhochschule Nordwestschweiz, Olten, Switzerland

    Thomas Hanne

  4. Department of Computer Science and information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Federal University of Bahia, Ondina, Brazil

    Tatiane Nogueira Rios

  6. Nantong University, Nantong Shi, Jiangsu, China

    Weiping Ding

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Kumar, P., Reji, R.E., Singh, V. (2022). Extracting Emotion and Sentiment Quotient of Viral Information Over Twitter. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_3

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