A Web-Based System for Emotion Vector Extraction | SpringerLink
Skip to main content
Springer Nature Link
Log in

A Web-Based System for Emotion Vector Extraction

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10406))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  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. 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: 978-1-4673-7678-5

    Google Scholar 

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  11. Wu, L., Hua, X.S., Yu, N., Ma, W.Y., Li, S.: Flickr Distance. Microsoft Research Asia, Beijing (2008)

    Book  Google Scholar 

  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. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Introduction to wordnet: an on-line lexical database (1993)

    Google Scholar 

  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

    Chapter  Google Scholar 

  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

    Chapter  Google Scholar 

  16. Newman, M.E.J.: Fast Algorithm for Detecting Community Structure in Networks. University of Michigan, Ann Arbor (2003)

    Google Scholar 

  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

    Chapter  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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: 978-1-4673-7678-5

    Google Scholar 

  21. Franzoni, V., Milani, A.: Heuristic semantic walk for concept chaining in collaborative networks. Int. J. Web Inf. Syst. 10(1), 85–103 (2014)

    Article  Google Scholar 

  22. Church, K.W., Hanks, P.: Word association norms, mutual information and lexicography. In: ACL, p. 27 (1989)

    Google Scholar 

  23. Turney P.: Mining the web for synonyms: PMI versus LSA on TEOFL. In: Proceedings of ECML (2001)

    Google Scholar 

  24. Lin, J.: Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theor. 37(1), 145–151 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  25. Cilibrasi, R., Vitanyi, P.: The Google Similarity Distance. ArXiv.org (2004)

    Google Scholar 

  26. Joyce, J.M.: Kullback-leibler divergence. In: Lovric, M. (ed.) International Encyclopedia of Statistical Science. Springer (2011)

    Google Scholar 

  27. Manning, D., Schutze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, London (2002)

    MATH  Google Scholar 

  28. Thurstone, L.: Attitudes can be measured. Am. J. Sociol. 33, 529–554 (1928)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  31. Osgood, C.E., Suci, G., Tannenbaum, P.: The Measurement of Meaning. University of Illinois Press, Urbana (1957)

    Google Scholar 

  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

    Chapter  Google Scholar 

  33. Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python. O’Reilly Media Inc., Sebastopol (2009)

    MATH  Google Scholar 

  34. http://www.seleniumhq.org/projects/webdriver

  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. 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. 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 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy

    Valentina Franzoni, Giulio Biondi & Alfredo Milani

  2. Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy

    Valentina Franzoni

  3. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Alfredo Milani

Authors
  1. Valentina Franzoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giulio Biondi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alfredo Milani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giulio Biondi .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Franzoni, V., Biondi, G., Milani, A. (2017). A Web-Based System for Emotion Vector Extraction. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10406. Springer, Cham. https://doi.org/10.1007/978-3-319-62398-6_46

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62398-6_46

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62397-9

  • Online ISBN: 978-3-319-62398-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation