Abstract
Adolescents are experiencing different psychological pressures coming from study, communication, affection, and self-recognition. If these psychological pressures cannot properly be resolved, it will turn to mental problems, which might lead to serious consequences. Traditional face-to-face psychological diagnosis and treatment cannot meet the demand of relieving teenagers’ stress completely due to its lack of timeliness and diversity. With micro-blog becoming a popular media channel for teenagers’ information acquisition, interaction, self-expression, emotion release, we envision a micro-blog platform to sense psychological pressures through teenagers’ tweets, and assist teenagers to release their stress through micro-blog. We investigate a number of features that may reveal teenagers’ pressures from their tweets, and then test five classifiers (Naive Bayes, Support Vector Machines, Artificial Neural Network, Random Forest, and Gaussian Process Classifier) for pressure detection. We also present ways to aggregate single-tweet based detection results in time series to overview teenagers’ stress fluctuation over a period of time. Experimental results show that the Gaussian Process Classifier offers the highest detection accuracy due to its robustness in the presence of a large degree of uncertainty that may be encountered with previously-unseen training data on tweets. Among the features, tweet’s emotional degree combining negative emotional words, emoticons, exclamation and question marks, plays a primary role in psychological pressure detection.
Keywords
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References
Sohu news (2012), http://learning.sohu.com/s2012/shoot/
Sohu news (2013), http://learning.sohu.com/20130402/n371458123.shtml
Sohu news (2012), http://learning.sohu.com/20120316/n337991511.shtml
Xue, Y., Li, Q., Feng, L., et al.: Towards a micro-blog platform for sensing and easing adolescent psychological pressures. In: Proc. of the 15th ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 215–218 (2013)
Andreevskaia, A., Bergler, S.: Mining wordnet for fuzzy sentiment: Sentiment tag extraction from wordnet glosses. In: Proc. of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL), pp. 209–216 (2006)
Ku, L.W., Wu, T.H., Lee, L.Y., et al.: Construction of an evaluation corpus for opinion extraction. In: Proc. of the Intl. Conf. on NTCIR, pp. 513–520 (2005)
Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proc. of the 40th Annual Meeting on Assoc. for Computational Linguistics, ACL (2002)
Nasukawa, T., Yi, J.: Sentiment analysis: Capturing favorability using natural language processing. In: Proc. of the 2nd International Conference on Knowledge Capture (K-CAP), pp. 70–77 (2003)
Ku, L., Sun, C.: Calculating emotional score of words for user emotion detection in messenger logs. In: Proc. of the 13th IEEE Intl. Conf. on Information Reuse and Integration: Workshop on Empirical Methods for Recognizing Inference in Text II (EM-RIT), pp. 138–143 (2012)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proc. of the Intl. Conf. on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)
Davidov, D., Tsur, O., Rappoport, A.: Enhanced sentiment learning using twitter hashtags and smileys. In: Proc. of the 23rd International Conference on Computational Linguistics, pp. 241–249 (2010)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Stanford University, Tech. Rep. (2009), http://www.stanford.edu/alecmgo/papers/TwitterDistantSupervision09.pdf
Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proc. of the 43rd Meeting of the Association for Computational Linguistics, ACL (2005)
Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach. In: Proc. of the 20th ACM Conf. on Information and Knowledge Management (CIKM), pp. 1031–1040 (2011)
Barbosa, L., Feng, J.: Robust sentiment detection on twitter from biased and noisy data. In: Proc. of the 23rd Intl Conf. on Computational Linguistics (COLING), pp. 36–44 (2010)
Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Prediction depression via social media. In: Proc. of the 7th Intl AAAI Conf. on Weblogs and Social Media (CWSM), pp. 128–137 (2013)
Choudhury, M., Counts, S., Horvitz, E.: Social media as a measurement tool of depression in populations. In: Proc. of the ACM Web Science, pp. 47–56 (2013)
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)
Che, W., Li, Z., Li, Y., Guo, Y., Qin, B., Liu, T.: Multilingual dependency-based syntactic and semantic parsing. In: Proc. of CoNLL, pp. 49–54 (2009)
Che, W., Li, Z., Liu, T.: Ltp: a chinese language technology platform. In: Proc. of Coling, pp. 13–16 (2010)
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Xue, Y., Li, Q., Jin, L., Feng, L., Clifton, D.A., Clifford, G.D. (2014). Detecting Adolescent Psychological Pressures from Micro-Blog. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_10
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DOI: https://doi.org/10.1007/978-3-319-06269-3_10
Publisher Name: Springer, Cham
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