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A Web-Based System for Emotion Vector Extraction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10406)

Abstract

The ability of assessing the affective information content is of increasing interest in applications of computer science, e.g. in human machine interfaces, recommender systems, social robots. In this project, the architecture of a semantic system of emotions is designed and implemented, to quantify the emotional content of short sentences by evaluating and aggregating the semantic proximity of each term in the sentence from the basic emotions defined in a psychological model of emotions (e.g. Ekman, Plutchick, Lovheim). Our model is parametric with respect to the semantic proximity measures, focusing on web-based proximity measures, where data needed to evaluate the proximity can be retrieved from search engines on the Web. To test the performances of the model, a software system has been developed to both collect the statistical data and perform the emotion analysis. The system automatizes the phases of sentence preprocessing, search engine query, results parsing, semantic proximity calculation and the final phase of ranking of emotions.

Keywords

Web document retrieval Semantic similarity measures Emotion recognition Affective data Affective computing 

Notes

Acknowledgements

Authors thank Mr. Ka Ho Tam, MSc and Dr. Yuanxi Li, PhD of the Hong Kong Baptist University, for the useful support and revision of the first version before submission.

References

  1. 1.
    Chiancone, A., Niyogi, R., et al.: Improving link ranking quality by quasi common neighbourhood. In: IEEE CPS 2015, International Conference on Computational Science and Its Applications (2015)Google Scholar
  2. 2.
    Chiancone, A., Madotto, A., et al.: Multistrain bacterial model for link prediction. In: Proceedings of 11th International Conference on Natural Computation IEEE ICNC 2015. CFP15CNC-CDR (2015). ISBN: 978-1-4673-7678-5Google Scholar
  3. 3.
    Chiancone, A., Franzoni, V., Li, Y., Markov, K., Milani, A.: Leveraging zero tail in neighbourhood based link prediction. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 3, pp. 135–139 (2015)Google Scholar
  4. 4.
    Franzoni, V., Poggioni, V., Zollo, F.: Automated book classification according to the emotional tags of the social network Zazie. In: ESSEM, AI*IA, vol. 1096, pp. 83–94. CEUR-WS (2013)Google Scholar
  5. 5.
    Franzoni, V., Leung, C.H.C., Li, Y., Milani, A., Pallottelli, S.: Context-based image semantic similarity. In: 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, pp. 1280–1284 (2015)Google Scholar
  6. 6.
    Franzoni, V., Milani, A.: Context extraction by multi-path traces in semantic networks, In: CEUR-WS, Proceedings of RR 2015 Doctoral Consortium, Berlin (2015)Google Scholar
  7. 7.
    Deng, J.J., Leung, C.H.C., Milani, A., Chen, L.: Emotional states associated with music: classification, prediction of changes, and consideration in recommendation. ACM Trans. Interact. Intell. Syst. 5, 4 (2015)CrossRefGoogle Scholar
  8. 8.
    Leung, C.H.C., Li, Y., Milani, A., Franzoni, V.: Collective evolutionary concept distance based query expansion for effective web document retrieval. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013. LNCS, vol. 7974, pp. 657–672. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-39649-6_47 CrossRefGoogle Scholar
  9. 9.
    Matsuo, Y., Sakaki, T., Uchiyama, K., Ishizuka, M.: Graph-based word clustering using a web search engine. University of Tokio (2006)Google Scholar
  10. 10.
    Franzoni, V., Milani, A.: A semantic comparison of clustering algorithms for the evaluation of web-based similarity measures. In: Gervasi, O., et al. (eds.) ICCSA 2016. LNCS, vol. 9790, pp. 438–452. Springer, Cham (2016). doi: 10.1007/978-3-319-42092-9_34 CrossRefGoogle Scholar
  11. 11.
    Wu, L., Hua, X.S., Yu, N., Ma, W.Y., Li, S.: Flickr Distance. Microsoft Research Asia, Beijing (2008)CrossRefGoogle Scholar
  12. 12.
    Budanitsky, A., Hirst, G.: Semantic distance in wordnet: an experimental, application-oriented evaluation of five measures. In: Proceedings of Workshop on WordNet and Other Lexical Resources, Pittsburgh, PA, USA, p. 641. North American Chapter of the Association for Computational Linguistics (2001)Google Scholar
  13. 13.
    Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Introduction to wordnet: an on-line lexical database (1993)Google Scholar
  14. 14.
    Tasso, S., Pallottelli, S., Ferroni, M., Bastianini, R., Laganà, A.: Taxonomy management in a federation of distributed repositories: a chemistry use case. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012. LNCS, vol. 7333, pp. 358–370. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-31125-3_28 CrossRefGoogle Scholar
  15. 15.
    Tasso, S., Pallottelli, S., Bastianini, R., Lagana, A.: federation of distributed and collaborative repositories and its application on science learning objects. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011. LNCS, vol. 6784, pp. 466–478. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21931-3_36 CrossRefGoogle Scholar
  16. 16.
    Newman, M.E.J.: Fast Algorithm for Detecting Community Structure in Networks. University of Michigan, Ann Arbor (2003)Google Scholar
  17. 17.
    Pallottelli, S., Tasso, S., Pannacci, N., Costantini, A., Lago, N.F.: Distributed and collaborative learning objects repositories on grid networks. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds.) ICCSA 2010. LNCS, vol. 6019, pp. 29–40. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-12189-0_3 CrossRefGoogle Scholar
  18. 18.
    Franzoni, V., Milani, A.: PMING distance: a collaborative semantic proximity measure. In: WI–IAT, vol. 2, pp. 442–449. IEEE/WIC/ACM (2012)Google Scholar
  19. 19.
    Franzoni, V., Milani, A.: Heuristic semantic walk. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013. LNCS, vol. 7974, pp. 643–656. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-39649-6_46 CrossRefGoogle Scholar
  20. 20.
    Franzoni, V., Milani, A., Pallottelli, S.: Multi-path traces in semantic graphs for latent knowledge elicitation. In: Proceedings of 11th International Conference on Natural Computation, IEEE ICNC (2015). ISBN: 978-1-4673-7678-5Google Scholar
  21. 21.
    Franzoni, V., Milani, A.: Heuristic semantic walk for concept chaining in collaborative networks. Int. J. Web Inf. Syst. 10(1), 85–103 (2014)CrossRefGoogle Scholar
  22. 22.
    Church, K.W., Hanks, P.: Word association norms, mutual information and lexicography. In: ACL, p. 27 (1989)Google Scholar
  23. 23.
    Turney P.: Mining the web for synonyms: PMI versus LSA on TEOFL. In: Proceedings of ECML (2001)Google Scholar
  24. 24.
    Lin, J.: Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theor. 37(1), 145–151 (1991)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Cilibrasi, R., Vitanyi, P.: The Google Similarity Distance. ArXiv.org (2004)Google Scholar
  26. 26.
    Joyce, J.M.: Kullback-leibler divergence. In: Lovric, M. (ed.) International Encyclopedia of Statistical Science. Springer (2011)Google Scholar
  27. 27.
    Manning, D., Schutze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, London (2002)zbMATHGoogle Scholar
  28. 28.
    Thurstone, L.: Attitudes can be measured. Am. J. Sociol. 33, 529–554 (1928)CrossRefGoogle Scholar
  29. 29.
    Stouffer, S.A., Guttman, L., et al.: Measurement and prediction. In: Studies in Social Psychology in World War II, vol. 4. Princeton University Press (1950)Google Scholar
  30. 30.
    Bartholomeu, D., Silva, M., Montiel, J.: Improving the likert scale of the children’s social skills test by means of rasch model. Psychology 7, 820–828 (2016)CrossRefGoogle Scholar
  31. 31.
    Osgood, C.E., Suci, G., Tannenbaum, P.: The Measurement of Meaning. University of Illinois Press, Urbana (1957)Google Scholar
  32. 32.
    Franzoni, V., Leung, Clement H.C., Li, Y., Mengoni, P., Milani, A.: Set similarity measures for images based on collective knowledge. In: Gervasi, O., Murgante, B., Misra, S., Gavrilova, M.L., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2015. LNCS, vol. 9155, pp. 408–417. Springer, Cham (2015). doi: 10.1007/978-3-319-21404-7_30 CrossRefGoogle Scholar
  33. 33.
    Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python. O’Reilly Media Inc., Sebastopol (2009)zbMATHGoogle Scholar
  34. 34.
  35. 35.
    Strapparava, C., Mihalcea, R.: SemEval-2007 task 14: affective text. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval 2007), pp. 70–74. Association for Computational Linguistics, Stroudsburg, PA, USA (2007)Google Scholar
  36. 36.
    Franzoni, V., Milani, A.: Semantic context extraction from collaborative networks. In: IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2015. IEEE Press (2015)Google Scholar
  37. 37.
    Franzoni, V., Milani, A.: A pheromone-like model for semantic context extraction from collaborative networks. In: 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapore, pp. 540–547. IEEE Press (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of PerugiaPerugiaItaly
  2. 2.Department of Computer, Control, and Management EngineeringSapienza University of RomeRomeItaly
  3. 3.Department of Computer ScienceHong Kong Baptist UniversityKowloon TongHong Kong

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