Abstract
Depression is a common mental health disorder. Despite its high prevalence, the only way of diagnosing depression is through self-reporting. However, 70% of the patients would not consult doctors at an early stage of depression. Meanwhile people increasingly relying on social media for sharing emotions, and daily life activities thus helpful for detecting their mental health. Inspired by these a total of 179 depressive individuals selected from Twitter, who have reported depression and they are on medical treatment. A sample of their recent tweets collected ranges from (200 to 3200) tweets per person. From their tweets, we selected 100 most frequently used words using Term Frequency-Inverse Document Frequency (TF-IDF). Later, we used the 14 psychological attributes in Linguistic Inquiry and Word Count (LIWC) to classify these words into emotions. Moreover, weights were assigned to each word from happy to unhappy after classification by LIWC and trained machine learning classifiers to classify the users into three classes of depression High, Medium, and Low. According to our study, better features selections and their combination will help to improve performance and accuracy of classifiers.
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References
Murray, C.J., Lopez, A.D.: Alternative projections of mortality and dis-ability by cause 1990–2020: global burden of disease study. Lancet. 349(9064), 1498–1504 (1997)
Hur, N.W., Kim, H.C., Waite, L., Youm, Y.: Is the relationship between depression and c reactive protein level moderated by social support in elderly?-Korean Social Life, Health, and Aging Project (KSHAP). Psychiatry Investig. 15(1), 24 (2018)
Liu, A., Liu, B., Lee, D., Weissman, M., Posner, J., Cha, J., Yoo, S.: Machine learning aided prediction of family history of depression. In: 2017 New York Scientific Data Summit (NYSDS), pp. 1–4. IEEE (2017)
Tadesse, M.M., Lin, H., Xu, B., Yang, L.: Detection of depression-related posts in reddit social media forum. IEEE Access. 7, 44883–44893 (2019)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text re-trieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Calvo, R.A., Milne, D.N., Hussain, M.S., Christensen, H.: Natural language processing in mental health applications using non-clinical texts. Nat. Lang. Eng. 23(5), 649–685 (2017)
Khalil, R.M., Al-Jumaily, A.: Machine learning based prediction of depression among type 2 diabetic patients. In: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 1–5. IEEE (2017)
Shen, G., et al.: Depression detection via harvesting social media: a multimodal dictionary learning solution. In: IJCAI (2017)
Billings, A.G., Moos, R.H.: Coping, stress, and social resources among adults with unipolar depression. J. Pers. Soc. Psychol. 46(4), 877 (1984)
De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)
Moreno, M.A., Jelenchick, L.A., Egan, K.G., Cox, E., Young, H., Gannon, K.E., Becker, T.: Feeling bad on Facebook: depression disclosures by college students on a social networking site. Depress. Anxiety. 28(6), 447–455 (2011)
Park, M., Cha, C., Cha, M.: Depressive moods of users portrayed in twitter. In: Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics (HI-KDD), vol. 2012, pp. 1–8 (2012)
Deshpande, M., Rao, V.: Depression detection using emotion artificial intelligence. In: 2017 International Conference on Intelligent Sustainable Systems (ICISS), pp. 858–862. IEEE (2017)
Seah, J.H., Shim, K.J.: Data mining approach to the detection of suicide in social media: a case study of Singapore. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 5442–5444. IEEE (2018)
Dieris-Hirche, J., Bottel, L., Bielefeld, M., Steinbuechel, T., Kehyayan, A., Dieris, B., te Wildt, B.: Media use and internet addiction in adult depression: a case-control study. Comput. Hum. Behav. 68, 96–103 (2017)
Twitter Developer Aplication Programming API. https://developer.twitter.com/. Online. Accessed 1 July 2016
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)
Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242 (2003)
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Mustafa, R.U., Ashraf, N., Ahmed, F.S., Ferzund, J., Shahzad, B., Gelbukh, A. (2020). A Multiclass Depression Detection in Social Media Based on Sentiment Analysis. In: Latifi, S. (eds) 17th International Conference on Information Technology–New Generations (ITNG 2020). Advances in Intelligent Systems and Computing, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-030-43020-7_89
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DOI: https://doi.org/10.1007/978-3-030-43020-7_89
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