Skip to main content
SpringerLink
Log in

A Deep Learning Semantic Approach to Emotion Recognition Using the IBM Watson Bluemix Alchemy Language

  • Conference paper
  • First Online:
Book cover 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

Sentiment analysis and emotion recognition are emerging research fields of research that aim to build intelligent systems able to recognize and interpret human emotions. Due to the applicability of these systems to almost all kinds of markets, also the interest of companies and industries is grown in an exponential way in the last years and a lot of frameworks for programming these systems are introduced. IBM Watson is one of the most famous and used: it offers, among others, a lot of services for Natural Language Processing. In spite of broad-scale multi-language services, most of functions are not available in a lot of “secondary” languages (like Italian). The main objective of this work is to demonstrate the feasibility of a translation-based approach to emotion recognition in texts written in “secondary” languages. We present a prototypical system using IBM Watson to extract emotions from Italian text by means of Bluemix Alchemy Language. Some preliminary results are shown and discussed in order to stress pro and cons of the approach.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • 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

References

  1. Anusha, V., Sandhya, B.: A learning based emotion classifier with semantic text processing. In: El-Alfy, E.-S.M., Thampi, S.M., Takagi, H., Piramuthu, S., Hanne, T. (eds.) Advances in Intelligent Informatics. AISC, vol. 320, pp. 371–382. Springer, Cham (2015). doi:10.1007/978-3-319-11218-3_34

    Google Scholar 

  2. Baioletti, M., Milani, A., Poggioni, V., Rossi, F.: An ACO approach to planning. In: Cotta, C., Cowling, P. (eds.) EvoCOP 2009. LNCS, vol. 5482, pp. 73–84. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01009-5_7

    Chapter  Google Scholar 

  3. Baioletti, M., Milani, A., Poggioni, V., Rossi, F.: Ant search strategies for planning optimization. In: ICAPS 2009 Proceedings of the 19th International Conference on Automated Planning and Scheduling, pp. 334–337 (2009)

    Google Scholar 

  4. Baioletti, M., Milani, A., Poggioni, V., Rossi, F.: Optimal planning with ACO. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS, vol. 5883, pp. 212–221. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10291-2_22

    Chapter  Google Scholar 

  5. Bhaskar, J., Sruthi, K., Nedungadi, P.: Hybrid approach for emotion classification of audio conversation based on text and speech mining. Procedia Comput. Sci. 46, 635–643 (2015)

    Article  Google Scholar 

  6. Chiancone, A., Franzoni, V., Milani, A.: A multistrain bacterial diffusion model for link prediction. Int. J. Pattern Recogn. Artif. Intell. 31(11), 157–172 (2017). World Scientific

    Google Scholar 

  7. Chiancone, A., Milani, A., Poggioni, V., Pallottelli, S., Madotto, A., Franzoni, V.: A multistrain bacterial model for link prediction. In: Proceedings of the International Conference on Natural Computation, pp. 1075–1079. IEEE Press (2016). doi:10.1109/ICNC.2015.7378141

  8. Ferrucci, D.A.: Introduction to this is watson. IBM J. Res. Dev. 56(34), 1 (2012)

    Google Scholar 

  9. Franzoni, V., Mencacci, M., Mengoni, P., Milani, A.: Semantic heuristic search in collaborative networks: measures and contexts. In: Proceedings 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI/IAT 2014, vol. 1, pp. 187–217. IEEE Press (2014). doi:10.1109/WI-IAT.2014.27

  10. Franzoni, V., Milani, A.: Pming distance: a collaborative semantic proximity measure. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2012, vol. 2, pp. 442–449. IEEE Press (2012). doi:10.1109/WI-IAT.2012.226

  11. Franzoni, V., Milani, A.: A pheromone-like model for semantic context extraction from collaborative networks. In: Proceedings IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015, 2016-January, pp. 540–547, IEEE Press (2016)

    Google Scholar 

  12. Franzoni, V., Poggioni, V., Zollo, F.: Can we infer book classification by blurbs. CEUR Workshop Proceedings, vol. 1127, pp. 16–19. CEUR WS (2014)

    Google Scholar 

  13. Franzoni, V., Biondi, G., Milani, A., Li, Y.: Web-based semantic similarity for emotion recognition in web objects. CoRR abs/1612.05734 (2016)

    Google Scholar 

  14. Franzoni, V., Poggioni, V., Zollo, F.: Automated classification of book blurbs according to the emotional tags of the social network zazie. In: 1st International Workshop on Emotion and Sentiment in Social and Expressive Media, ESSEM 2013, CEUR Workshop Proceedings, pp. 83–94. CEUR WS (2013)

    Google Scholar 

  15. Gentili, E., Milani, A., Poggioni, V.: Data summarization model for user action log files. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012. LNCS, vol. 7335, pp. 539–549. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31137-6_41

    Chapter  Google Scholar 

  16. Gupta, R.K., Yang, Y.: Crystalnest at semeval-2017 task 4: Using sarcasm detection for enhancing sentiment classification and quantification. In: SemEval: 11th International Workshop on Semantic Evaluation, Aug 3–4, 2017, Vancouver, Canada (to appear)

    Google Scholar 

  17. High, R.: The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works. IBM Corporation, Redbooks, Armonk (2012)

    Google Scholar 

  18. Houjeij, A., Hamieh, L., Mehdi, N., Hajj, H.: A novel approach for emotion classification based on fusion of text and speech. In: 2012 19th International Conference on Telecommunications (ICT), pp. 1–6, April 2012

    Google Scholar 

  19. Huang, S.l., Chen, Y.S.: Developing document classifiers for recognizing article readers’ affects. In: Proceedings of the 2012 International Conference on Information Management (2012)

    Google Scholar 

  20. Liberati, C., Camillo, F.: Subjective business polarization: Sentiment analysis meets predictive modeling. In: Catania, B., et al. (eds.) New Trends in Databases and Information Systems. AISC, vol. 241. Springer, Cham (2014)

    Chapter  Google Scholar 

  21. Lupan, D., Bobocescu-Kesikis, S., Dascalu, M., Trausan-Matu, S., Dessus, P.: Predicting readers’ emotional states induced by news articles through latent semantic analysis. In: SMART 2013 International Conference on Social Media in Academia: Research and Teaching, pp. 79–84. Citeseer (2013)

    Google Scholar 

  22. Mancini, L., Milani, A., Poggioni, V., Chiancone, A.: Self regulating mechanisms for network immunization. AI Commun. 29(2), 301–317 (2016)

    Article  MathSciNet  Google Scholar 

  23. Markines, B., Cattuto, C., Menczer, F.: Social spam detection. In: Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web AIRWeb 2009, p. 41 (2009)

    Google Scholar 

  24. Milani, A., Poggioni, V.: Planning in reactive environments. Comput. Intell. 23(4), 439–463 (2007)

    Article  MathSciNet  Google Scholar 

  25. Pallottelli, S., Franzoni, V., Milani, A.: Multi-path traces in semantic graphs for latent knowledge elicitation. In: Proceedings of International Conference on Natural Computation 2016-January, pp. 281–288. IEEE Press (2016). doi:10.1109/ICNC.2015.7378004

  26. Ren, F., Quan, C.: Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing. Inf. Technol. Manage. 13(4), 321–332 (2012)

    Article  Google Scholar 

  27. Shelke, N.: Approaches of emotion detection from text. Int. J. Comput. Sci. Inf. Technol. Res. 2(2), 123–128 (2014)

    Google Scholar 

  28. Shivhare, S.N., Garg, S., Mishra, A.: Emotionfinder: detecting emotion from blogs and textual documents. In: 2015 International Conference on Computing, Communication & Automation (ICCCA), pp. 52–57. IEEE (2015)

    Google Scholar 

  29. Shivhare, S.N., Saritha, S.K.: Emotion detection from text documents. Int. J. Data Min. Knowl. Manage. Process 4(6), 51 (2014)

    Article  Google Scholar 

  30. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  31. Vallverdú, J., Trovato, G.: Emotional affordances for humanrobot interaction. Adapt. Behav. 24(5), 320–334 (2016)

    Article  Google Scholar 

  32. Vanzo, A., Croce, D., Castellucci, G., Basili, R., Nardi, D.: Spoken language understanding for service robotics in Italian. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS, vol. 10037, pp. 477–489. Springer, Cham (2016). doi:10.1007/978-3-319-49130-1_35

    Chapter  Google Scholar 

  33. Wang, H., Xu, H., Liu, L., Song, W., Du, C.: An unsupervised microblog emotion dictionary construction method and its application on sentiment analysis. J. Inf. Comput. Sci. 12, 2729–2739 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Giulio Biondi & Valentina Poggioni

  2. Department of Computer, Control and Management Engineering, Sapienza University of Rome, Via Ariosto, 25, Rome, Italy

    Valentina Franzoni

Authors
  1. Giulio Biondi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Valentina Franzoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Valentina Poggioni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valentina Franzoni .

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

Biondi, G., Franzoni, V., Poggioni, V. (2017). A Deep Learning Semantic Approach to Emotion Recognition Using the IBM Watson Bluemix Alchemy Language. 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_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62398-6_51

  • 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