Semantic Patterns for Sentiment Analysis of Twitter

  • Hassan Saif
  • Yulan He
  • Miriam Fernandez
  • Harith Alani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8797)


Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.


Sentiment Analysis Semantic Patterns Twitter 


  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, Portland, Oregon (2011)Google Scholar
  2. 2.
    Aisopos, F., Papadakis, G., Varvarigou, T.: Sentiment analysis of social media content using n-gram graphs. In: Proc. the 3rd ACM International Workshop on Social Media (2011)Google Scholar
  3. 3.
    Asiaee, T.A., Tepper, M., Banerjee, A., Sapiro, G.: If you are happy and you know it... tweet. In: Proc. the 21st ACM Conference on Information and Knowledge Management (2012)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Bravo-Marquez, F., Mendoza, M., Poblete, B.: Combining strengths, emotions and polarities for boosting twitter sentiment analysis. In: Proc. the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (2013)Google Scholar
  6. 6.
    Cambria, E., Hussain, A.: Sentic computing: Techniques, tools, and applications, vol. 2. Springer (2012)Google Scholar
  7. 7.
    Diakopoulos, N., Shamma, D.: Characterizing debate performance via aggregated twitter sentiment. In: Proc. 28th Int. Conf. on Human Factors in Computing Systems. ACM (2010)Google Scholar
  8. 8.
    Gangemi, A., Presutti, V., Reforgiato Recupero, D.: Frame-based detection of opinion holders and topics: A model and a tool. IEEE Computational Intelligence Magazine (2014)Google Scholar
  9. 9.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford (2009)Google Scholar
  10. 10.
    Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: The good the bad and the omg! In: Proceedings of the ICWSM, Barcelona, Spain (2011)Google Scholar
  11. 11.
    Lin, C., He, Y., Everson, R., Ruger, S.: Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data Engineering 24(6), 1134–1145 (2012)CrossRefGoogle Scholar
  12. 12.
    Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50(2), 159–179 (1985)CrossRefGoogle Scholar
  13. 13.
    Mohammad, S.M., Kiritchenko, S., Zhu, X.: Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242 (2013)Google Scholar
  14. 14.
    Nakov, P., Rosenthal, S., Kozareva, Z., Stoyanov, V., Ritter, A., Wilson, T.: Semeval-2013 task 2: Sentiment analysis in twitter. In: Proc. the 7th ACL International Workshop on Semantic Evaluation (2013)Google Scholar
  15. 15.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC 2010, Valletta, Malta (2010)Google Scholar
  16. 16.
    Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proc. The 2003 Conference on Empirical Methods in Natural Language Processing (2003)Google Scholar
  17. 17.
    Saif, H., Fernandez, M., He, Y., Alani, H.: Evaluation datasets for twitter sentiment analysis a survey and a new dataset, the sts-gold. In: Proceedings, 1st ESSEM Workshop, Turin, Italy (2013)Google Scholar
  18. 18.
    Saif, H., Fernandez, M., He, Y., Alani, H.: On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter. In: Proc. 9th Language Resources and Evaluation Conference (LREC), Reykjavik, Iceland (2014)Google Scholar
  19. 19.
    Saif, H., Fernandez, M., He, Y., Alani, H.: Senticircles for contextual and conceptual semantic sentiment analysis of twitter. In: Proc. 11th Extended Semantic Web Conf. (ESWC), Crete, Greece (2014)Google Scholar
  20. 20.
    Saif, H., He, Y., Alani, H.: Alleviating data sparsity for twitter sentiment analysis. In: Proc. Workshop on Making Sense of Microposts (#MSM2012) in WWW 2012, Lyon, France (2012)Google Scholar
  21. 21.
    Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of twitter. In: Proc. 11th Int. Semantic Web Conf. (ISWC), Boston, MA (2012)Google Scholar
  22. 22.
    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, Edinburgh, Scotland (2011)Google Scholar
  23. 23.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. American Society for Information Science and Technology 63(1), 163–173 (2012)CrossRefGoogle Scholar
  24. 24.
    Thet, T.T., Na, J.C., Khoo, C.S., Shakthikumar, S.: Sentiment analysis of movie reviews on discussion boards using a linguistic approach. In: Proc. the 1st International CIKM Workshop on Topic-sentiment Analysis for Mass Opinion (2009)Google Scholar
  25. 25.
    Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), Philadelphia, Pennsylvania (2002)Google Scholar
  26. 26.
    Turney, P.D., Pantel, P., et al.: From frequency to meaning: Vector space models of semantics. Journal of artificial Intelligence Research 37(1), 141–188 (2010)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada (2005)Google Scholar
  28. 28.
    Wittgenstein, L.: Philosophical Investigations. Blackwell, London (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hassan Saif
    • 1
  • Yulan He
    • 2
  • Miriam Fernandez
    • 1
  • Harith Alani
    • 1
  1. 1.Knowledge Media InstituteThe Open UniversityUnited Kingdom
  2. 2.School of Engineering and Applied ScienceAston UniversityUnited Kingdom

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