Abstract
The increasingly faster life pace in modern society makes people always feel stressful and it is of great significance to discover a users suffering stress in time. According to psychological study, shopping is chosen as an effective way for stress relief, especially for females. Compared with non-stress cases, a user may perform different shopping patterns when under stress. An interesting issue then arises: can we detect one’s psychological stress from online shopping data? By investigating stress-related outlier features from both content and behavior of online purchase orders, we learn a users stress status by classification. A real user study of 20 experienced female online customers aged 23–30 verifies the effectiveness of shopping based stress detection, achieving an F1-measure of more than 80 % with J48 classifier. None of the features negatively affect the detection result. Feature combinations bring dramatic improvements than single feature. In total, shopping content features are proved to be more significant than behavior features.
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Notes
- 1.
http://www.imdb.com/title/tt0502439/, a television series in 1991.
- 2.
http://www.taobao.com, the biggest C2C e-commerce site in China.
- 3.
1. New Year’s Day 2. Spring Festival 3. Valentines’ Day 4. Women’s Day 5. Easter Day 6. May Day 7. Mother’s Day 8. Father’s Day 9. Dragon Boat Festival 10. Children’s Day 11. National Day 12. Teachers’ Day 13. Mid-Autumn Day 14. Halloween 15. Thanksgiving Day 16. Christmas Day 17. Lantern’s Day.
References
Arnold, M.J., Reynolds, K.E.: Hedonic shopping motivations. J. Retail. 79(2), 77–95 (2003)
Atalay, A.S., Meloy, M.G.: Retail therapy: a strategic effort to improve mood. Psychol. Market. 28(6), 638–659 (2011)
Gregoire, C.: Retail therapy: one in three recently stressed americans shops to deal with anxiety (2013). http://www.huffingtonpost.com/2013/05/23/retail-therapy-shopping_n_3324972.html
Choudhury, M., Counts, S., Horvitz, E.: Social media as a measurement tool of depression in populations. In: ACM Web Science, pp. 47–56 (2013)
Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Prediction depression via social media. In: ICWSM, pp. 128–137 (2013)
Cohen, S., Kamarck, T., Mermelstein, R.: A global measure of perceived stress. J. Health Soc. Behav. 4, 385–396 (1983)
Hamid, N.H.A., Sulaiman, N., Aris, S., Murat, Z., Taib, M.: Evaluation of human stress using eeg power spectrum. In: CSPA, pp. 1–4 (2010)
Hosseini, S., Khalilzadeh, M.: Emotional stress recognition system using EEG and psychophysiological signals: using new labelling process of EEG signals in emotional stress state. In: ICBECS, pp. 1–6 (2010)
Jin, L., Xue, Y., Li, Q., Feng, L.: Integrating human mobility and social media for adolescent psychological stress detection. In: Navathe, S.B., et al. (eds.) DASFAA 2016. LNCS, vol. 9643, pp. 367–382. Springer, Heidelberg (2016). doi:10.1007/978-3-319-32049-6_23
Lee, E., Moschis, G.P., Mathur, A.: A study of life events and changes in patronage preferences. J. Bus. Res. 54(1), 25–38 (2001)
Leith, K.P., Baumeister, R.F.: Why do bad moods increase self-defeating behavior? emotion, risk tasking, and self-regulation. J. Person. Soc. Psychol. 71(6), 1250–1267 (1996)
Li, Q., Xue, Y., Jia, J., Feng, L.: Helping teenagers relieve psychological pressures: a micro-blog based system. In: EDBT, pp. 660–663 (2014)
Lin, H., Jia, J., Guo, Q., Xue, Y., Huang, J., Cai, L., Feng, L.: Psychological stress detection from cross-media microblog data using deep sparse neural nework. In: ICME, pp. 1–6 (2014)
Lin, H., Jia, J., Guo, Q., Xue, Y., Li, Q., Huang, J., Cai, L., Feng, L.: User-level psychological stress detection from social media using deep neural network. In: MM, pp. 507–516 (2014)
Shen, Y.-C., Kuo, T.-T., Yeh, I.-N., Chen, T.-T., Lin, S.-D.: Exploiting temporal information in a two-stage classification framework for content-based depression detection. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 276–288. Springer, Heidelberg (2013)
Shi, Y., Ruiz, N., Taib, R., Choi, E., Chen, F.: Galvanic skin response (GSR) as an index of cognitive load. In: CHI, pp. 2651–2656 (2007)
Thayer, R.E., Newman, J.R., McClain, T.M.: Self-regulation of mood: strategies for changing a bad mood, raising energy, and reducing tension. J. Person. Soc. Psychol. 67(5), 910–925 (1994)
Tice, D.M., Bratslavsky, E.: Giving in to feel good: the place of emotion regulation in the context of general self-control. Psychol. Inq. 11(3), 149–159 (2000)
Tice, D.M., Bratslavsky, E., Baumeister, R.F.: Emotional distress regulation takes precedence over impulse control: if you feel bad, do it!. J. Person. Soc. Psychol. 80(1), 53–67 (2001)
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 Workshops. LNCS, vol. 7867, pp. 201–213. Springer, Heidelberg (2013)
Xue, Y., Li, Q., Jin, L., Feng, L., Clifton, D.A., Clifford, G.D.: Detecting adolescent psychological pressures from micro-blog. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds.) HIS 2014. LNCS, vol. 8423, pp. 83–94. Springer, Heidelberg (2014)
Zhang, Y., Tang, J., Sun, J., Chen, Y., Rao, J.: Moodcast: emotion prediction via dynamic continuous factor graph model. In: ICDM, pp. 1193–1198 (2010)
Zhao, L., Jia, J., Feng, L.: Teenagers stress detection based on time-sensitive micro-blog comment/response actions. In: Dillon, T. (ed.) IFIP AI 2015. IFIP AICT, vol. 465, pp. 26–36. Springer, Heidelberg (2015)
Acknowledgement
The work is supported by National Natural Science Foundation of China (61373022, 61532015, 71473146) and Chinese Major State Basic Research Development 973 Program (2015CB352301).
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Zhao, L., Wang, H., Xue, Y., Li, Q., Feng, L. (2016). Psychological Stress Detection from Online Shopping. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_35
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