An Information-Theoretical Method for Emotion Classification
Identifying the emotion that someone is feeling will allow to improve the experience of the person interaction with environments, devices, and contents. Our body responds to events around us, by emotional responses, reflected in cognitive, behavioral and physiological dimensions. In the present work, we target the electrocardiogram (ECG) response as a mean to express emotions. Its processing is performed using information-theoretical measures, allowing true exploratory data mining. Participants recruited for the experiment watched three video sets in three different days, with a different emotion being induced in each day: fear, happiness, and neutral condition. The method is divided in: (1) conversion of the real-valued ECG record into a symbolic time-series; (2) relative compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as a reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. An accuracy of 90% was obtained. A posteriori analysis of the false negative results indicated that there was a relation between the relative dissimilarity measure and the self-reported emotions.
KeywordsEmotion Affective computing Classification Data compression Kolmogorov complexity
This work is funded by national funds, European Regional Development Fund, FSE through COMPETE2020, through FCT, in the scope of the framework contract foreseen in the numbers 4, 5 and 6 of the article 23, of the Decree-Law 57/2016, of August 29, changed by Law 57/2017, of July 19; and in the scope of the projects UID/CEC/00127/2013 (IEETA/UA), CMUP-ERI/FIA/0031/2013 (VR2Market). J. M. Carvalho acknowledges the Doctoral Grant from FCT, ref. SFRH/BD/136815/2018.
Conflict of Interest
- 8.Brás, S., Ferreira, J., Soares, S., Pinho, A.: Biometric and emotion identification: an ECG compression based method. Frontiers Psychol. 9 (2018)Google Scholar
- 13.Berntson, G.G., Quigley, K.S., Lozano, D.: Cardiovascular psychophysiology. In: Handbook of Psychophysiology, 3rd edn., pp. 182–210. Cambridge University Press (2007)Google Scholar
- 14.Pinho, A.J., Ferreira, P.J.S.G.: Image similarity using the normalized compression distance based on finite context models. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 1993–1996. IEEE (2011)Google Scholar
- 15.Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11. ACM (2003)Google Scholar
- 16.Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of the 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)Google Scholar