Systematical Approach for Detecting the Intention and Intensity of Feelings on Social Network

IEEE Journal of Biomedical and Health Informatics

Volumn 20, Issue 4, 2016, Pages 987-995

  Tai, Chih Hua ^a   Tan, Zheng Han ^a   Chang, Yue Shan ^a  

^a NATIONAL TAIPEI UNIVERSITY   (Taiwan)

Author keywords

Feeling intensity; latent Dirichlet allocation (LDA); supervised LDA (sLDA)

Indexed keywords

BEHAVIORAL RESEARCH; CHEMICAL DETECTION; DISEASES; STATISTICS;

DISTINGUISHERS; FEELING INTENSITY; LATENT DIRICHLET ALLOCATION; LATENT DIRICHLET ALLOCATIONS; MENTAL ILLNESS; POSITIVE FEELINGS; SENTIWORDNET; SUPERVISED LDA (SLDA);

SOCIAL NETWORKING (ONLINE);

EXPERIMENTAL MODEL; HUMAN; SOCIAL NETWORK; BEHAVIOR; EMOTION; MEDICAL INFORMATICS; SOCIAL MEDIA; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

EMOTIONS; HUMANS; INTENTION; MEDICAL INFORMATICS; MODELS, STATISTICAL; SOCIAL MEDIA;

EID: 84978245828     PISSN: 21682194     EISSN: 21682208     Source Type: Journal    
DOI: 10.1109/JBHI.2016.2535721     Document Type: Article

References (26)

1. S. Bao, S. Xu, L. Zhang, R. Yan, Z. Su, D. Han, and Y. Yu, "Mining social emotions from affective text, " IEEE Trans. Knowl. Data Eng., vol. 24, no. 9, pp. 1658-1670, Sep. 2012.
2. M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, andA.Kappas, "Sentiment strength detection in short informal text, " J. Amer. Soc. Inf. Sci. Technol., vol. 61, no. 12, pp. 2544-2558, 2010.
3. M. Yassine and H. Hajj, "A framework for emotion mining from text in online social networks, " in Proc. IEEE Int. Conf. DataMiningWorkshops, 2010, pp. 1136-1142.
4. M. D. Choudhury, S. Counts, E. J. Horvitz, and A. Hoff, "Characterizing and predicting postpartum depression from shared Facebook data, " in Proc. 17th ACM Conf. Comput. Supported Cooperative Work Soc. Comput., 2014, pp. 626-638.
5. M. D. Choudhury, M. Gamon, S. Counts, and E. Horvitz, "Predicting depression via social media, " presented at the 7th International AAAI Conference on Weblogs and Social Media, Boston, MA, USA, 2013.
6. G. Coppersmith, C. Harman, and M. Dredze, "Measuring post traumatic stress disorder in Twitter, " presented at the International AAAI Conference on Weblogs and Social Media, Ann Arbor, MI, USA, 2014.
7. T. Nguyen, D. Phung, B. Dao, S. Venkatesh, and M. Berk, "Affective and content analysis of online depression communities, " IEEE Trans. Affective Comput., vol. 5, no. 3, pp. 217-226, Jul.-Sep. 2014.
8. American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders V. Washington, DC, USA: Amer. Psychiatric Press, 2013.
9. J. D. Mcauliffe and D. M. Blei, "Supervised topic models, " presented at the Advances in Neural Information Processing Systems, Vancouver, B.C., Canada, 2007.
10. D. Ramage, D. Hall, R. Nallapati, and C. D. Manning, "Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora, " in Proc. Conf. Empirical Methods Natural Language Process., 2009, pp. 248-256.
11. D. Blei, A. Ng, and M. Jordan, "Latent Dirichlet allocation, " J. Mach. Learning Res., vol. 3, pp. 993-1022, 2003.
12. A. Esuli and F. Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining, " presented at the Conference on Language Resources and Evaluation, Genoa, Italy, 2006.
13. P. Chikersal, S. Poria, and E. Cambria, "SeNTU: Sentiment analysis of tweets by combining a rule-based classifier with supervised learning, " presented at the InternationalWorkshop on Semantic Evaluation, Denver, Colorado, USA, 2015.
14. M. Hasan, E. Agu, and E. Rundensteiner, "Using hashtags as labels for supervised learning of emotions in twitter messages, " presented at the ACM SIGKDDWorkshop on Health Informatics, New York, USA, 2014.
15. J. A. Russell, "A circumplexmodel of affect, " J. Personality Soc. Psychol., vol. 39, no. 6, pp. 1161-1178, 1980.
16. S. Volkova, Y. Bachrach, M. Armstrong and V. Sharma, "Inferring latent user properties from texts published in social media, " presented at the 29th AAAI Conference on Artificial Intelligence, Austin, Texas, USA, 2015.
17. T. Minka and J. Lafferty, "Expectation-propagation for the generative aspect model, " presented at the Conference on Uncertainty in Artificial Intelligence, Alberta, Canada, 2002.
18. T. L. Griffiths and M. Steyvers, "Finding scientific topics, " Proc. Nat. Acad. Sci., vol. 101, no. 1, pp. 5228-5235, 2004.
19. D. Quercia, H. Askham, and J. Crowcroft, "TweetLDA: Supervised topic classification and link prediction in twitter, " in Proc. Annu. ACM Web Sci. Conf., 2012, pp. 247-250.
20. T. N. Rubin, A. Chambers, P. Smyth, and M. Steyvers, "Statistical topic models for multi-label document classification, " Mach. Learning, vol. 88, nos. 1/2, pp. 157-208, 2012.
21. S. Baccianella, A. Esuli, and F. Sebastiani, "SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, " presented at the Conference on International Language Resources and Evaluation, Malta, 2010.
22. Y. S. Chang, C. T. Fan, W. T. Lo, W. C. Hung and S. M. Yuan, "Mobile cloud-based depression diagnosis using an ontology and a Bayesian network, " Future Gener. Comput. Syst., vol. 43-44, pp. 87-98, 2015.
23. S. Crystal, U. Sambamoorthi, J. T. Walkup, and A. Akincigil, "Diagnosis and treatment of depression in the elderly medicare population: Predictors, disparities, and trends, " J. Amer. Geriatrics Soc., vol. 51, no. 12, 1718-1728, 2003.
24. Z. Rihmer, "Can better recognition and treatment of depression reduce suicide rates? A brief review, " Eur. Psychiatry, vol. 16, no. 7, pp. 406-409, 2001.
25. B. W. Rovner, R. J. Casten, M. T. Hegel, B. E. Leiby, and W. S. Tasman, "Preventing depression in age-related macular degeneration, " Arch. Gen. Psychiatry, vol. 64, no. 8, pp. 886-892, 2007.
26. B. G. Tiemens, J. Ormel, J. A. Jenner, K. Van Der Meer, T. W. D. P. Van Os, R. H. S. Van Den Brink, A. Smit, and W. Van Den Brink, "Training primary-care physicians to recognize, diagnose and manage depression: Does it improve patient outcomes?" Psychol. Med., vol. 29, no. 4, pp. 833-845, 1999.