Advertisement

A Framework to Rank Nodes in Social Media Graph Based on Sentiment-Related Parameters

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 409)

Abstract

Social networks provide a platform for users to interact and engage in various activities. Information pertaining to social media can be shared, ideas can be put forward and opinions can be analysed. Sentiment analysis of user comments can be done to extract important information and to make informed decisions. This paper elucidates previous work done on sentiment analysis and different ranking techniques for utilisation in different applications. A methodology is proposed in this paper for ranking users based on parameters such as likes, shares and user comments. Two ranking techniques are proposed in the methodology. One technique is based on the cosine similarity and the other involves features such as user comments.

Keywords

Social media Facebook Access token Sentiment analysis Ranking 

References

  1. 1.
    Alkeinaya, N. Y., & Norwawi, N. M. (2014). User oriented privacy model for social networks. Journal of Procedia: Social and Behavioral Sciences, 129, 191–197.Google Scholar
  2. 2.
    Kunpeng, Z., Yu, C., Yusheng, X., Honbo, D., Agrawal, A., Palsetia, D., Lee, K., Wei-keng, L., & Choudhary, A. (2011). SES: Sentiment elicitation system for social media data. In IEEE 11th International Conference on Data Mining Workshops (ICDMW), Vancouver (pp. 129–136).Google Scholar
  3. 3.
    Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15–21.CrossRefGoogle Scholar
  4. 4.
    Chen, H., & Zimbra, D. (2010). AI and opinion mining. IEEE Intelligent Systems, 25(3), 74–76.CrossRefGoogle Scholar
  5. 5.
    Liu, B. (2010). Sentiment analysis: A multifaceted problem. IEEE Intelligent Systems, 25(3), 76–80.Google Scholar
  6. 6.
    Snyder, B., & Barzilay, R. (2007). Multiple aspect ranking using the good grief algorithm. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Article no. N07-1038, pp. 300–307.Google Scholar
  7. 7.
    Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Annual Conference of the Empirical Methods in Natural Language Processing (Vol. 10, pp. 79–86).Google Scholar
  8. 8.
    Pang, P., & Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Association for Computational Linguistics (pp. 115–1240).Google Scholar
  9. 9.
    Turney, P. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Association for Computational Linguistics (pp. 417–424).Google Scholar
  10. 10.
    Kamps, J. (2004). Using WordNet to measure semantic orientation of adjectives. In Proceedings of the 4th Annual International Conference of Language Resources and Evaluation, European Language Resources Association (pp. 1115–1118).Google Scholar
  11. 11.
    Riloff, E., & Wiebe, J. (2003). Learning extraction patterns for subjective expressions. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (pp. 105–112).Google Scholar
  12. 12.
    Kim, S., & Hovy, E. (2006). Extracting opinions, opinion holders, and topics expressed in online news media text. In Proceedings of the Workshop on Sentiment and Subjectivity in Text (pp. 1–8).Google Scholar
  13. 13.
    Santidhanyaroj, P., Khan, T. A.; Gelowitz, C. M., & Benedicenti, L. (2014). A sentiment analysis prototype system for social network data. In IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), Toronto, Canada (pp. 1–5).Google Scholar
  14. 14.
    Choi, Y., & Cardie, C. (2008). Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 793–801).Google Scholar
  15. 15.
    Liu, J., & Seneff, S. (2009). Review sentiment scoring via parse and-paraphrase paradigm. In Proceedings of Conference on Empirical Methods in Natural Language Processing (vol. 1, pp. 161–169).Google Scholar
  16. 16.
    Zhao, Y., Niu, K., He, Z., Lin, J., & Wang, X. (2013). Text sentiment analysis algorithm optimization and platform development in social network. In Sixth International Symposium on Computational Intelligence and Design (pp. 410–413).Google Scholar
  17. 17.
    Ortega, F. J., Troyano, J. A., Cruz, F. L., Vallejo, C. G., & Enríquez, F. (2012). Propagation of trust and distrust for the detection of trolls in a social network. Journal of Computer Networks, 56, 2884–2895.CrossRefGoogle Scholar
  18. 18.
    Reilly, C. F., Salinas, D., & De Leon, D. (2014). Ranking users based on influence in a directional social network. In International Conference on Computational Science and Computational Intelligence, Las Vegas (Vol. 2, pp. 237–240).Google Scholar
  19. 19.
    Subbian, K., & Melville, P (2011). Supervised rank aggregation for predicting influencers in twitter. In: IEEE International Conference on Privacy, Security, Risk, and Trust. IEEE Third International Conference on Social Computing (pp. 661–665).Google Scholar
  20. 20.
    Liang, B., Liu, Y., Zhang, M., Ma, S., Ru, L., & Zhang, K. (2014). Searching for people to follow in social networks. Journal of Expert Systems with Applications, 41, 7455–7465.CrossRefGoogle Scholar
  21. 21.
    Min, M., Choi, D., Kim. J., & Lee, J. H. (2011). The identification of intimate friends in personal social network. In International Conference on Computational Aspects of Social Networks (CASoN), Salamanca (pp. 233–236).Google Scholar
  22. 22.
    Eirinaki, M., Pisal, S., & Singh, J. (2012). Feature-based opinion mining and ranking. Journal of Computer and System Sciences, 78, 1175–1184.CrossRefMathSciNetGoogle Scholar
  23. 23.
    Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network service use. Journal of Computers in Human Behavior, 26(2), 254–263.CrossRefGoogle Scholar
  24. 24.
    Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5, 1093–1113.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  1. 1.University Institute of Engineering and TechnologyPanjab UniversityChandigarhIndia

Personalised recommendations