Page 164 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

Chapter 5 Depression discovery in cancer communities using deep learning 153

[67] N. Zainuddin, A. Selamat, R. Ibrahim, Discovering hate sentiment within
twitter data through aspect-based sentiment analysis, J. Phys. Conf. 1447 (1)
(January, 2020) 012056. IOP Publishing.
[68] F.T. Giuntini, M.T. Cazzolato, M.D.J.D. dos Reis, A.T. Campbell, A.J. Traina,
J. Ueyama, A review on recognizing depression in social networks:
challenges and opportunities, J. Ambient Intell. Humaniz. Comput. (2020)
1e17.
[69] J.W. Pennebaker, C.K. Chung, M. Ire-land, A. Gonzales, R.J. Booth, The
Development and Psychometric Properties of LIWC2007 the University of
Texas at Austin, 2007. Technical Report 2.
[70] S. Jamison-Powell, C. Linehan, L. Daley,Andrew Garbett, S. Lawson, “I can't
get no sleep”: discussing #insomnia on Twitter, in: Conference on Human
Factors in Computing Systems - Proceedings, 2012, pp. 1501e1510.
[71] M.T. Lehrman, C.O. Alm, Ruben A. Proano, Detecting distressed and non-
distressed affect states in short forum texts, in: Second Workshop on
Language in Social Media, LSM, 2012, pp. 9e18. Montreal.
[72] M. De Choudhury, S. Counts, E. Horvitz, Major life changes and behavioral
markers in social media : case of childbirth, in: Computer Supported
Cooperative Work (CSCW), 2013, pp. 1431e1442.
[73] H. Andrew Schwartz, J. Eichstaedt, M. LKern, G. Park, M. Sap, D. Stillwell,
M. Kosinski, L. Ungar, Towards assessing changes in degree of depression
through Facebook, in: Proceedings of the Workshop on Computational
Linguistics and Clinical Psychology: From Linguistic Signal to Clinical
Reality, 2014, pp. 118e125.
[74] V. Arya, R. Agrawal, Improvement in text categorization using semi-
supervised approach and lexical chains, J. Comput. Theor. Nanosci. 16 (12)
(2019) 5122e5126.
[75] M. De Choudhury, Role of social media in tackling challenges in mental
health, in: Proceedings of the 2nd International Workshop on Socially-
Aware Multimedia (SAM’13), 2013, pp. 49e52.
[76] D. Preotiuc-Pietro, M. Sap, H. Andrew Schwartz, L. Ungar, Mental lllness
detection at the world well-being project for the CLPsych 2015 shared task,
in: Proceedings of the 2nd Workshop on Computational Linguistics and
Clinical Psychology: From Linguistic Signal to Clinical Reality, 2015,
pp. 40e45.
[77] J.W. Pennebaker, The Secret Life of Pronouns: What Our Words Say about
Us, Bloomsbury Press, 2011.
[78] T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient Estimation of Word
Representations in Vector Space.CoRR, arXiv preprint arXiv:1301.3781.,
2013. pp. 1e12 (David N Milne, Glen Pink, Ben Hachey, and CoRR).
[79] M. Faruqui, J. Dodge, K. Sujay, J. ChrisDyer, E. Hovy, N.A. Smith,
Retrofitting word vectors to semantic lexicons, in: The 2015 Conference of
the North American Chapter of the Association for Computational
Linguistics - Human Language Technologies (NAACL HLT2015), Denver.
Felix Hill, Kyunghyu, 2015.
[80] R. Collobert, J. Weston, A unified architecture for natural language
processing: deep neural networks with multitask learning, in: Proceedings
of the 25th International Conference on Machine Learning, ACM, New
York, 2008, pp. 160e167.
[81] F. Hill, K. Cho, A. Korhonen, Learning Distributed Representations of
Sentences From Unlabelled Data, 2016, pp. 1367e1377.