Advertisement

Estimating Public Opinion in Social Media Content Using Aspect-Based Opinion Mining

  • Yen Hong TranEmail author
  • Quang Nhat Tran
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 235)

Abstract

With the development of the Internet, social media has been the main platform for human to express opinions about products/services, key figures, socio-political and economic events… Besides the benefits that the platform offers, there are still various security threats relating to the fact that most extremist groups have been abusing social media to spread distorted beliefs, to incite the act of terrorism, politics, religions, to recruit, to raise funds and much more. These groups tend to include sentiment leading to illegal affairs such as terrorism, cyber-attacks, etc. when sharing their opinions and comments. Therefore, it is necessary to capture public opinions and social behaviors in social media content. This is a challenging research topic related to aspect-based opinion mining, which is the problem of determining what the exact opinions on specific aspects are rather than getting an overall positive or negative sentiment at the document level. For an entity, the main task is to detect all mentioned aspects of the entity and then produce a summary of each aspect’s sentiment orientation. This paper proposes an aspect-based opinion mining model to address the problem of estimating public opinion in social media content. The model has two phases: 1 - extracting aspects based on double propagation techniques, and 2 - classifying opinions about the detected aspects with the consideration of the context of review sentences using the hybrid approach of machine learning and lexicon-based method.

Keywords

Aspect-based opinion mining Aspect extraction Sentiment orientation Public opinion analysis Natural language processing Text mining Social behavior 

References

  1. 1.
    Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael ©2012Google Scholar
  2. 2.
    Khan, K., Baharudin, B., Khan, A.: Mining opinion components from unstructured reviews. 26(3), 258–275 (2014)Google Scholar
  3. 3.
    Zhang, L., Liu, B.: Aspect and entity extraction for opinion mining. In: Chu, W. (ed.) Data Mining and Knowledge Discovery for Big Data. SBD 2004, vol. 1, pp. 1–40. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-40837-3_1Google Scholar
  4. 4.
    Maynard, D., Funk, A.: Automatic detection of political opinions in tweets. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 88–99. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-25953-1_8CrossRefGoogle Scholar
  5. 5.
    Potha, N.: A biology-inspired, data mining framework for extracting patterns in sexual cyberbullying data. Knowl.-Based Syst. 96, 134–155 (2016)CrossRefGoogle Scholar
  6. 6.
    Zhao, R., Zhou, A., Mao, K.: Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking, ICDCN 2016, Singapore (2016)Google Scholar
  7. 7.
    Sui, J.: Doctor of Philosophy: Understanding and fighting bullying with machine learning. University of Wisconsin-Madison (2015)Google Scholar
  8. 8.
    Lippmann, R.P., et al.: Toward finding malicious cyber discussions in social media. Presented at the The AAAI-17 Workshop on Artificial Intelligence for Cyber Security (2017)Google Scholar
  9. 9.
    Azizan, S.A., Aziz, I.A.: Terrorism detection based on sentiment analysis using machine learning. J. Eng. Appl. Sci. 12, 691–698 (2017)Google Scholar
  10. 10.
    Wen, S., Haghighi, M.S., Chen, C., Xiang, Y., Zhou, W.L., Jia, W.J.: A sword with two edges: propagation studies on both positive and negative information in online social networks. IEEE Trans. Comput. 64, 640–653 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data MiningGoogle Scholar
  12. 12.
    Hu, M., Liu, B.: Mining opinion features in customer reviews. Presented at the AAAI 2004 Proceedings of the 19th National Conference on Artifical Intelligence, pp. 755–760 (2004)Google Scholar
  13. 13.
    Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT 2005, pp. 339–346 (2005)Google Scholar
  14. 14.
    Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI 2009, pp. 1199–1204 (2009)Google Scholar
  15. 15.
    Zhai, Z., Liu, B., Xu, H., Jia, P.: Grouping product features using semi-supervised learning with soft-constraints. In: Proceedings of the 23rd International Conference on Computational Linguistics (2010)Google Scholar
  16. 16.
    Raju, S., Shishtla, P., Varma, V.: Graph clustering approach to product attribute extraction. Presented at the 4th Indian International Conference on Artificial Intelligence (2009)Google Scholar
  17. 17.
    Zhai, Z., Liu, B., Xu, H., Jia, P.: Clustering product features for opinion mining. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (2011)Google Scholar
  18. 18.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)Google Scholar
  19. 19.
    Hatzivassiloglou, V., McKeown, K.: Predicting the semantic orientation of adjectives. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (1997)Google Scholar
  20. 20.
  21. 21.
    Budanitsky, A., Hirst, G.: Semantic distance in wordnet: an experimental, application-oriented evaluation of five measures. Presented at the Workshop on WordNet and Other Lexical Resources (2001)Google Scholar
  22. 22.
    Hu, Liu: Opinion Lexicon: A list of English positive and negative opinion words or sentiment words. https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html#lexicon
  23. 23.
    Ringsquandl, M., Petković, D.: Analyzing political sentiment on Twitter. In: 2013 AAAI Spring Symposium, Stanford University (2013)Google Scholar
  24. 24.
    Html Agility Pack. http://html-agility-pack.net
  25. 25.

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.People’s Security AcademyHanoiVietnam
  2. 2.University of New South Wales at ADFACanberraAustralia

Personalised recommendations