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Erschienen in:
Human Brainnetome Atlas and Its Potential Applications in Brain-Inspired Computing Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Sentiment Analysis and Affective Computing: Methods and Applications

verfasst von : Barbara Calabrese, Mario Cannataro

Erschienen in: Brain-Inspired Computing

Verlag: Springer International Publishing

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Abstract

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.

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Literatur
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)
2.
Martinez, C.C., Cassol, M.: Measurement of voice quality, anxiety and depression symptoms after speech therapy. J. Voice 29(4), 446–449 (2015)CrossRef
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)CrossRef
4.
5.
Koelstra, S., Patras, I.: Fusion of facial expressions and EEG for implicit affective tagging. Image Vis. Comput. 31, 164–174 (2013)CrossRef
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)CrossRef
7.
Pang, B., Lee, L.: Opinion mining and sentiment analysis. J. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRef
8.
Zafarani, R., Liu, H.: Behavior analysis in social media. IEEE Intell. Syst. 29(4), 9–11 (2014)CrossRef
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 CrossRef
10.
Snyder, M.: Self-monitoring of expressive behavior. J. Pers. Soc. Psychol. 30(4), 526–537 (1974)CrossRef
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)CrossRef
12.
Armony, J.L.: Affective computing. Trends Cogn. Sci. 2(7), 270 (1998)CrossRef
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)CrossRef
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)
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)
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)CrossRef
17.
Dai, W., Han, D., Dai, Y., Xu, D.: Emotion recognition and affective computing on vocal social media. Inform. Manage. 52, 777–788 (2015)CrossRef
18.
Lee, Y.Y., Hsieh, S.: Classifying different emotional states by means of EEG-based functional connectivity patterns. PLoS ONE 9, 1–13 (2014)
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)CrossRef
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)
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 CrossRef
22.
Bhuvaneswari, P., Kumar, J.S.: Support vector machine technique for EEG signals. Int. J. Comput. Appl. 63(13), 1–5 (2013)
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)
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)CrossRef
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)CrossRef
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)CrossRef
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 CrossRef
28.
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)CrossRef
29.
Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl.-Based Syst. 89, 14–46 (2015)CrossRef
30.
Feldman, R.: Techniques and applications for sentiment analysis. Mag. Commun. ACM 56(4), 82–89 (2013)CrossRef
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)CrossRef
32.
Batrinca, B., Treleaven, P.: C.,: Social media analytics: a survey of techniques, tools and platforms. AI Soc. 30, 89–116 (2015)CrossRef
33.
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Technical report. Stanford University, Stanford Digital Library Technologies Project (2009)
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)
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)
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)MathSciNetCrossRef
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)
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)
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)
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)CrossRef
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 CrossRef
42.
Poria, S., Cambria, E., Hussain, A., Huang, G.: Towards an intelligent framework for multimodal affective data analysis. Neural Netw. 63, 104–116 (2015)CrossRef
Metadaten
Titel
Sentiment Analysis and Affective Computing: Methods and Applications
verfasst von
Barbara Calabrese
Mario Cannataro
Copyright-Jahr
2016
Buch
Brain-Inspired Computing

Print ISBN: 978-3-319-50861-0

Electronic ISBN: 978-3-319-50862-7

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50862-7_13

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