Construction of a Multi-dimensional Vectorized Affective Lexicon

  • Yang Wang
  • Chong FengEmail author
  • Qian Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


Affective analysis has received growing attention from both research community and industry. However, previous works either cannot express the complex and compound states of human’s feelings or rely heavily on manual intervention. In this paper, by adopting Plutchik’s wheel of emotions, we propose a lowcost construction method that utilizes word embeddings and high-quality small seed-sets of affective words to generate multi-dimensional affective vector automatically. And a large-scale affective lexicon is constructed as a verification, which could map each word to a vector in the affective space. Meanwhile, the construction procedure uses little supervision or manual intervention, and could learn affective knowledge from huge amount of raw corpus automatically. Experimental results on affective classification task and contextual polarity disambiguation task demonstrate that the proposed affective lexicon outperforms other state-of-the-art affective lexicons.


Affective analysis Affective lexicon Knowledge representation 


  1. 1.
    Baccianella, S., Esuli, A., Sebastiani, F.: SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC 2010, vol. 10, pp. 2200–2204 (2010)Google Scholar
  2. 2.
    Cambria, E., Livingstone, A., Hussain, A.: The hourglass of emotions. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds.) Cognitive Behavioural Systems. LNCS, vol. 7403, pp. 144–157. Springer, Heidelberg (2012). Scholar
  3. 3.
    Cambria, E., Poria, S., Bajpai, R., Schuller, B.W.: SenticNet 4: a semantic resource for sentiment analysis based on conceptual primitives. In: COLING 2016, pp. 2666–2677 (2016)Google Scholar
  4. 4.
    Cambria, E., Speer, R., Havasi, C., Hussain, A.: SenticNet: a publicly available semantic resource for opinion mining. In: AAAI Fall Symposium: Commonsense Knowledge, vol. 10 (2010)Google Scholar
  5. 5.
    Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr, E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI 2010. vol. 5, p. 3 (2010)Google Scholar
  6. 6.
    Dong, Z., Dong, Q., Hao, C.: HowNet and its computation of meaning. In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 53–56 (2010)Google Scholar
  7. 7.
    Esuli, A., Sebastiani, F.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation 17, 1–26 (2007)Google Scholar
  8. 8.
    Fellbaum, C.: WordNet. Wiley Online Library, New York (1998)zbMATHGoogle Scholar
  9. 9.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, vol. 1, no. 12 (2009)Google Scholar
  10. 10.
    Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  11. 11.
    Ku, L.W., Liang, Y.T., Chen, H.H.: Opinion extraction, summarization and tracking in news and blog corpora. In: Proceedings of AAAI, pp. 100–107 (2006)Google Scholar
  12. 12.
    Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 142–150 (2011)Google Scholar
  13. 13.
    Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. arXiv (2013)Google Scholar
  14. 14.
    Nozza, D., Fersini, E., Messina, E.: A multi-view sentiment corpus. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, vol. 1, pp. 273–280 (2017)Google Scholar
  15. 15.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing-Volume 10, pp. 79–86. Association for Computational Linguistics (2002)Google Scholar
  16. 16.
    Plutchik, R.: The nature of emotions human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am. Sci. 89(4), 344–350 (2001)CrossRefGoogle Scholar
  17. 17.
    Ribeiro, F.N., Araújo, M., Gonçalves, P., Gonçalves, M.A., Benevenuto, F.: SentiBench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5(1), 1–29 (2016)CrossRefGoogle Scholar
  18. 18.
    Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. TKDE 28(3), 813–830 (2016)Google Scholar
  19. 19.
    Stojanovski, D., Strezoski, G., Madjarov, G., Dimitrovski, I.: Finki at SemEval-2016 task 4: deep learning architecture for Ttwitter sentiment analysis. In: SemEval 2016, pp. 149–154 (2016)Google Scholar
  20. 20.
    Strapparava, C., Valitutti, A., et al.: WordNet affect: an affective extension of WordNet. In: LREC, vol. 4, pp. 1083–1086 (2004)Google Scholar
  21. 21.
    Talavera, E., Radeva, P., Petkov, N.: Towards Egocentric Sentiment Analysis. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2017. LNCS, vol. 10672, pp. 297–305. Springer, Cham (2018). Scholar
  22. 22.
    Tang, D., Wei, F., Qin, B., Yang, N., Liu, T., Zhou, M.: Sentiment embeddings with applications to sentiment analysis. TKDE 28(2), 496–509 (2016)Google Scholar
  23. 23.
    Thelwall, M.: Heart and soul: sentiment strength detection in the social web with sentistrength. In:Cyberemotions: Collective emotions in cyberspace (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Beijing Institute of TechnologyBeijingChina

Personalised recommendations