Advertisement

Semantic Sentiment Analysis of Twitter

  • Hassan Saif
  • Yulan He
  • Harith Alani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7649)

Abstract

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

Keywords

Sentiment analysis semantic concepts feature interpolation 

References

  1. 1.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proc. ACL 2011 Workshop on Languages in Social Media, pp. 30–38 (2011)Google Scholar
  2. 2.
    Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proceedings of COLING, pp. 36–44 (2010)Google Scholar
  3. 3.
    Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., Smith, N.: Part-of-speech tagging for twitter: Annotation, features, and experiments. Tech. rep., DTIC Document (2010)Google Scholar
  4. 4.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford (2009)Google Scholar
  5. 5.
    Guerra, P., Veloso, A., Meira Jr., W., Almeida, V.: From bias to opinion: A transfer-learning approach to real-time sentiment analysis. In: Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD (2011)Google Scholar
  6. 6.
    Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg! In: Proceedings of the ICWSM (2011)Google Scholar
  7. 7.
    Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, pp. 375–384. ACM (2009)Google Scholar
  8. 8.
    Moschitti, A.: Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 318–329. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC 2010 (2010)Google Scholar
  10. 10.
    Rizzo, G., Troncy, R.: Nerd: Evaluating named entity recognition tools in the web of data. In: Workshop on Web Scale Knowledge Extraction (WEKEX 2011), vol. 21 (2011)Google Scholar
  11. 11.
    Saif, H., He, Y., Alani, H.: Semantic Smoothing for Twitter Sentiment Analysis. In: Proceeding of the 10th International Semantic Web Conference, ISWC (2011)Google Scholar
  12. 12.
    Saif, H., He, Y., Alani, H.: Alleviating Data Sparsity for Twitter Sentiment Analysis. In: Proceedings, 2nd Workshop on Making Sense of Microposts (#MSM 2012): Big Things Come in Small Packages: in Conjunction with WWW (2012)Google Scholar
  13. 13.
    Shamma, D., Kennedy, L., Churchill, E.: Tweet the debates: understanding community annotation of uncollected sources. In: Proceedings of the First SIGMM Workshop on Social Media, pp. 3–10. ACM (2009)Google Scholar
  14. 14.
    Speriosu, M., Sudan, N., Upadhyay, S., Baldridge, J.: Twitter polarity classification with label propagation over lexical links and the follower graph. In: Proceedings of the EMNLP First Workshop on Unsupervised Learning in NLP, pp. 53–63 (2011)Google Scholar
  15. 15.
    Tan, C., Lee, L., Tang, J., Jiang, L., Zhou, M., Li, P.: User-level sentiment analysis incorporating social networks. In: Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD (2011)Google Scholar
  16. 16.
    Yu, B.: An evaluation of text classification methods for literary study. Literary and Linguistic Computing 23(3), 327–343 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hassan Saif
    • 1
  • Yulan He
    • 1
  • Harith Alani
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityUnited Kingdom

Personalised recommendations