Sentiment Analysis and Affective Computing: Methods and Applications

  • Barbara Calabrese
  • Mario CannataroEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10087)


New computing technologies, such as affective computing and sentiment analysis, are raising a strong interest in different fields, such as marketing, politics and, recently, life sciences. Examples of possible applications in the last field, regard the detection and monitoring of depressive states or mood disorders and anxiety conditions. This paper aims to provide an introductory overview of affective computing and sentiment analysis, through the discussion of the main processing techniques and applications. The paper concludes with a discussion relative to a new approach based on the integration of sentiment analysis and affective computing to obtain a more accurate and reliable detection of emotions and feelings for applications in the life sciences.


Social network Life sciences Sentiment analysis Affective computing 



This work has been partially supported by the following research project funded by the Italian Ministry of University and Research (MIUR): PON03PE_00001_1 BA2Know-Business Analytics to Know.


  1. 1.
    Valstar, M.: Automatic behaviour understanding in medicine. In: Proceedings of the Workshop on Roadmapping the Future of Multimodal Interaction Research including Business Opportunities and Challenges, pp. 57–60 (2014)Google Scholar
  2. 2.
    Martinez, C.C., Cassol, M.: Measurement of voice quality, anxiety and depression symptoms after speech therapy. J. Voice 29(4), 446–449 (2015)CrossRefGoogle Scholar
  3. 3.
    Schaefer, K.L., Baumann, J., Rich, B.A., Luckenbaugh, D.A., Zarate, C.A.: Perception of facial emotion in adults with bipolar or unipolar depression and controls. J. Psychiatr. Res. 44, 1229–1235 (2010)CrossRefGoogle Scholar
  4. 4.
    Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)CrossRefGoogle Scholar
  5. 5.
    Koelstra, S., Patras, I.: Fusion of facial expressions and EEG for implicit affective tagging. Image Vis. Comput. 31, 164–174 (2013)CrossRefGoogle Scholar
  6. 6.
    Poria, S., Cambria, E., Howard, N., Huang, G., Hussain, A.: Fusing audio, visual and textual clues for sentiment analysis from multimodal content. Neurocomputing 174, 50–59 (2016)CrossRefGoogle Scholar
  7. 7.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. J. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  8. 8.
    Zafarani, R., Liu, H.: Behavior analysis in social media. IEEE Intell. Syst. 29(4), 9–11 (2014)CrossRefGoogle Scholar
  9. 9.
    Wang, X., Zhang, C., Ji, Y., Sun, L., Wu, L., Bao, Z.: A depression detection model based on sentiment analysis in micro-blog social network. In: Li, J., Cao, L., Wang, C., Tan, K.C., Liu, B., Pei, J., Tseng, V.S. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7867, pp. 201–213. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-40319-4_18 CrossRefGoogle Scholar
  10. 10.
    Snyder, M.: Self-monitoring of expressive behavior. J. Pers. Soc. Psychol. 30(4), 526–537 (1974)CrossRefGoogle Scholar
  11. 11.
    He, Q., Glas, C.A.W., Kosinski, M., Stillwell, D.J., Veldkamp, B.P.: Predicting self-monitoring skills using textual posts on Facebook. Comput. Hum. Behav. 33, 69–78 (2014)CrossRefGoogle Scholar
  12. 12.
    Armony, J.L.: Affective computing. Trends Cogn. Sci. 2(7), 270 (1998)CrossRefGoogle Scholar
  13. 13.
    Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1(1), 18–37 (2010)CrossRefGoogle Scholar
  14. 14.
    Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 39–58 (2009)Google Scholar
  15. 15.
    Lee, H., Choi, Y.S., Lee, S., Park, I.P.: Towards unobtrusive emotion recognition for affective social communication. In: 9th Annual IEEE Consumer Communications and Networking Conference, pp. 260–264. IEEE (2012)Google Scholar
  16. 16.
    Batliner, A., Schuller, B., Seppi, D., Steidl, S., Devillers, L., Vidrascu, L., Vogt, T., Aharonson, V., Amir, N.: The automatic recognition of emotions in speech. Emotion-Oriented Syst. 2, 71–99 (2011)CrossRefGoogle Scholar
  17. 17.
    Dai, W., Han, D., Dai, Y., Xu, D.: Emotion recognition and affective computing on vocal social media. Inform. Manage. 52, 777–788 (2015)CrossRefGoogle Scholar
  18. 18.
    Lee, Y.Y., Hsieh, S.: Classifying different emotional states by means of EEG-based functional connectivity patterns. PLoS ONE 9, 1–13 (2014)Google Scholar
  19. 19.
    Delle-Vignea, D., Wangb, W., Kornreicha, C., Verbancka, P., Campanellaa, S.: Emotional facial expression processing in depression: Data from behavioral and event-related potential studies. Neurophysiol. Clin. Clin. Neurophysiol. 44, 169–187 (2014)CrossRefGoogle Scholar
  20. 20.
    Klem, G.H., Luders, H.O., Jasper, H.H., Elger, C.: The ten - twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol. 52, 3–6 (1999)Google Scholar
  21. 21.
    Wang, X.-W., Nie, D., Lu, B.-L.: EEG-based emotion recognition using frequency domain features and support vector machines. In: Lu, B.-L., Zhang, L., Kwok, J. (eds.) ICONIP 2011. LNCS, vol. 7062, pp. 734–743. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24955-6_87 CrossRefGoogle Scholar
  22. 22.
    Bhuvaneswari, P., Kumar, J.S.: Support vector machine technique for EEG signals. Int. J. Comput. Appl. 63(13), 1–5 (2013)Google Scholar
  23. 23.
    Nie, D., Wang, X.W., Shi, L.C., Lu, B.L.:EEG-based emotion recognition during watching movies. In: 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 667–670 (2011)Google Scholar
  24. 24.
    Yoon, H.J., Chung, S.Y.: EEG-based emotion estimation using Bayesian weighted-log-posterior function and perceptron convergence algorithm. Comput. Biol. Med. 43(12), 2230–2237 (2013)CrossRefGoogle Scholar
  25. 25.
    Peter, C., Ebert, E., Beikirch, H.: A wearable multi-sensor system for mobile acquisition of emotion-related physiological data. Affect. Comput. Intell. Interac. 3784, 691–698 (2005)CrossRefGoogle Scholar
  26. 26.
    Ioannou, S.V., Raouzaiou, A.T., Tzouvaras, V.A., Mailis, T.P., Karpouzis, K.C., Kollias, S.D.: Emotion recognition through facial expression analysis based on a neurofuzzy network. Neural Netw. 18(4), 423–435 (2005)CrossRefGoogle Scholar
  27. 27.
    Barrón-Estrada, M.L., Zatarain-Cabada, R., Beltrán V., J.A., Cibrian R., F.L., Pérez, Y.H.: An intelligent and affective tutoring system within a social network for learning mathematics. In: Pavón, J., Duque-Méndez, N.D., Fuentes-Fernández, R. (eds.) IBERAMIA 2012. LNCS (LNAI), vol. 7637, pp. 651–661. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-34654-5_66 CrossRefGoogle Scholar
  28. 28.
    Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)CrossRefGoogle Scholar
  29. 29.
    Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl.-Based Syst. 89, 14–46 (2015)CrossRefGoogle Scholar
  30. 30.
    Feldman, R.: Techniques and applications for sentiment analysis. Mag. Commun. ACM 56(4), 82–89 (2013)CrossRefGoogle Scholar
  31. 31.
    Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., Herrera-Viedma, E.: Sentiment analysis: a review and comparative analysis of web services. Inform. Sci. 311, 18–38 (2015)CrossRefGoogle Scholar
  32. 32.
    Batrinca, B., Treleaven, P.: C.,: Social media analytics: a survey of techniques, tools and platforms. AI Soc. 30, 89–116 (2015)CrossRefGoogle Scholar
  33. 33.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report. Stanford University, Stanford Digital Library Technologies Project (2009)Google Scholar
  34. 34.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation, pp. 1320–1326 (2010)Google Scholar
  35. 35.
    Rodrigues, R.G., das Dores, R.M., Camilo-Junior, C.G., Rosa, T.C.: SentiHealth-Cancer: a sentiment analysis tool to help detecting mood of patients in online social networks. Int. J. Med. Inform. 85, 80–95 (2016)Google Scholar
  36. 36.
    Ortigosa, A., Carro, R.M., Quiroga, J.I.: Predicting user personality by mining social interactions in Facebook. J. Comput. Syst. Sci. 80, 57–71 (2014)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Martin, J.M., Ortigosa, A., Carro, R.M.: SentBuk: sentiment analysis for e-learning environments. In: 2012 International Symposium on Computers in Education (SIIE), pp. 1–6. IEEE (2012)Google Scholar
  38. 38.
    Gonçalves, P., Araújo, M., Benevenuto, F., Cha, M.: Comparing and combining sentiment analysis methods. In: Proceedings of the First ACM Conference on Online Social Networks (2013)Google Scholar
  39. 39.
    Araújo, M., Gonçalves, P., Cha, M., Benevenuto, F.: iFeel: a web system that compares and combines sentiment analysis methods. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion (2014)Google Scholar
  40. 40.
    Poria, S., Gelbukh, A., Cambria, E., Hussain, A., Huang, G.: EmoSenticSpace: a novel framework for affective common-sense reasoning. Knowl.-Based Syst. 69, 108–123 (2014)CrossRefGoogle Scholar
  41. 41.
    Calabrese, B., Cannataro, M., Ielpo, N.: Using social networks data for behavior and sentiment analysis. In: Fatta, G., Fortino, G., Li, W., Pathan, M., Stahl, F., Guerrieri, A. (eds.) IDCS 2015. LNCS, vol. 9258, pp. 285–293. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-23237-9_25 CrossRefGoogle Scholar
  42. 42.
    Poria, S., Cambria, E., Hussain, A., Huang, G.: Towards an intelligent framework for multimodal affective data analysis. Neural Netw. 63, 104–116 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.University Magna Græcia of CatanzaroCatanzaroItaly

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