Advertisement

Unsupervised Sentiment Analysis of Twitter Posts Using Density Matrix Representation

  • Yazhou Zhang
  • Dawei SongEmail author
  • Xiang Li
  • Peng Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)

Abstract

Nowadays, a series of pioneering studies provide the evidence that quantum probability theory can be applied in information retrieval as a mathematical framework, such as Quantum Language Model (QLM) and its variants. In these studies, the density matrix, which is defined on the quantum probabilistic space, is used to represent query and document. However, these studies are only designed for information retrieval tasks, which are unable to model sentiment information. In this paper, we investigate the feasibility of quantum probability theory for twitter sentiment analysis, and propose a density matrix based unsupervised sentiment analysis approach. The main idea is to artificially create two sentiment dictionaries, generate density matrices of documents and dictionaries using an extended QLM, then employ the quantum relative entropy to judge the similarity between density matrices of documents and dictionaries. Extensive experiments are conducted on two widely used twitter datasets, which are the Obama-McCain Debate (OMD) dataset and Sentiment Strength Twitter Dataset (SS-Tweet). The experimental results show that our approach significantly outperforms a number of baselines, demonstrating the effectiveness of the proposed density matrix based sentiment analysis approach.

Keywords

Sentiment analysis Quantum Language Model Density matrix 

Notes

Acknowledgements

This work is supported in part by the Chinese National Program on Key Basic Research Project (973 Program, grant No. 2014CB744604), Natural Science Foundation of China (grant No. U1636203, 61272265, 61402324), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 721321.

References

  1. 1.
    Giachanou, A., Crestani, F.: Like it or not: a survey of twitter sentiment analysis methods. ACM Comput. Surv. (CSUR) 49(2), 28 (2016)CrossRefGoogle Scholar
  2. 2.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  3. 3.
    Rosenthal, S., Farra, N., Nakov, P.: Semeval-2017 task 4: sentiment analysis in twitter. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 502–518. (2017)Google Scholar
  4. 4.
    Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266–6282 (2013)CrossRefGoogle Scholar
  5. 5.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, pp. 30–38. Association for Computational Linguistics (2011)Google Scholar
  6. 6.
    Lee, S., Jin, X., Kim, W.: Sentiment classification for unlabeled dataset using doc2vec with jst. In: Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World, p. 28. ACM (2016)Google Scholar
  7. 7.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of Empirical Methods in Natural Language Processing, pp. 79–86 (2002)Google Scholar
  8. 8.
    Yin, Y., Jin, Z.: Document sentiment classification based on the word embedding (2015)Google Scholar
  9. 9.
    Giatsoglou, M., Vozalis, M.G., Diamantaras, K., Vakali, A., Sarigiannidis, G., Chatzisavvas, K.C.: Sentiment analysis leveraging emotions and word embeddings. Expert Syst. Appl. 69, 214–224 (2017)CrossRefGoogle Scholar
  10. 10.
    Pham, D.H., Le, A.C.: Learning multiple layers of knowledge representation for aspect based sentiment analysis. Data Knowl. Eng. (2017)Google Scholar
  11. 11.
    Van Rijsbergen, C.J.: The Geometry of Information Retrieval. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  12. 12.
    Li, Q., Li, J., Zhang, P., Song, D.: Modeling multi-query retrieval tasks using density matrix transformation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 871–874. ACM (2015)Google Scholar
  13. 13.
    Sordoni, A., Nie, J.Y., Bengio, Y.: Modeling term dependencies with quantum language models for IR. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 653–662. ACM (2013)Google Scholar
  14. 14.
    Li, J., Zhang, P., Song, D., Hou, Y.: An adaptive contextual quantum language model. Phys. A 456, 51–67 (2016)CrossRefGoogle Scholar
  15. 15.
    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. Association for Computational Linguistics (2002)Google Scholar
  16. 16.
    Turney, P.D.: Mining the web for synonyms: PMI-IR versus LSA on TOEFL. In: De Raedt, L., Flach, P. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 491–502. Springer, Heidelberg (2001).  https://doi.org/10.1007/3-540-44795-4_42 CrossRefGoogle Scholar
  17. 17.
    Goncalves, D.S., Gomes-Ruggiero, M.A., Lavor, C.: Global convergence of diluted iterations in maximum-likelihood quantum tomography. arXiv preprint arXiv:1306.3057 (2013)
  18. 18.
    Schumacher, B., Westmoreland, M.D.: Relative entropy in quantum information theory. Contemp. Math. 305, 265–290 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol. 10 (2010)Google Scholar
  20. 20.
    Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the omg!. ICWSM 11(164), 538–541 (2011)Google Scholar
  21. 21.
    Diakopoulos, N.A., Shamma, D.A.: Characterizing debate performance via aggregated twitter sentiment. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1195–1198. ACM (2010)Google Scholar
  22. 22.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Assoc. Inf. Sci. Technol. 63(1), 163–173 (2012)CrossRefGoogle Scholar
  23. 23.
    Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. Int. Conf. Mach. Learn. 4(2), 1188 (2014)Google Scholar
  24. 24.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Assoc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyTianjin UniversityTianjinChina
  2. 2.School of Computing and CommunicationsThe Open UniversityMilton KeynesUK

Personalised recommendations