Page 161 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
P. 161
150 Chapter 5 Depression discovery in cancer communities using deep learning
[17] W. Zhang, H. Xu, W. Wan, Weakness finder: find product weakness from
Chinese reviews by using aspects based sentiment analysis, Expert Syst.
Appl. 39 (2012) 10283e10291.
[18] A. Moreo, M. Romero, J.L. Castro, J.M. Zurita, Lexicon-based comments-
oriented news sentiment analyzer system, Expert Syst. Appl. 39 (2012)
9166e9180.
[19] B. Heerschop, F. Goossen, A. Hogenboom, F. Frasincar, U. Kaymak,
F.D. Jong, Polarity analysis of texts using discourse structure, in: 20th ACM
Conference on Information and Knowledge Management (CIKM’11), 2011.
[20] W.C. Mann, S.A. Thompson, Rhetorical structure theory: description and
construction of text structures, in: Third International Workshop on Text
Generation, Netherlands, 1986.
[21] C. Zirn, M. Niepert, H. Stuckenschmidt, M. Strube, Fine-grained sentiment
analysis with structural features, in: 5th International Joint Conference on
Natural Language Processing, 2011.
[22] Y. Wu, F. Ren, Learning sentimental influence in twitter, in: International
Conference on Future Computer Sciences and Application (ICFCSA), Hong
Kong, 2011.
[23] B.O. Connor, R. Balasubramanyan, B.R. Routledge, N.A. Smith, From tweets
to polls: linking text sentiment to public opinion time series, in:
Proceedings of Fourth International AAAI Conference on Weblogs and
Social Media, 2010.
[24] T. Wilson, P. Hoffmann, S. Somasundaran, J. Kessler, J. Wiebe, Y. Choi,
C. Cardie, E. Riloff, S. Patwardhan, OpinionFinder: a system for subjectivity
analysis, in: HLT-demo '05 Proceedings of HLT/EMNLP on Interactive
Demonstration, Vancouver, British Columbia, Canada, October 07, 2005.
[25] T.T. Thet, J.C. Na, C.S. Khoo, Aspect-based sentiment analysis of movie
reviews on discussion boards, J. Inf. Sci. 36 (6) (2010) 823e848.
[26] A. Esuli, F. Sebastiani, SentiWordNet: a publicly available lexical resource
for opinion mining, in: Proceedings of the 5th Conference on Language
Resources and Evaluation (LREC 2006), 2006, pp. 417e422. Genova.
[27] V. Singh, R. Piryani, A. Uddin, P. Waila, Sentiment analysis of movie
reviews: a new feature-based heuristic for aspect-level sentiment
classification, in: Proceedings of the 2013 International Muli-Conference on
Automation, Communication, Computing, Control and Compressed
Sensing, 2013, pp. 712e717. Kerala-India.
[28] T. Nasukawa, J. Yi, Sentiment Analysis: Capturing Favorability Using
Natural Language Processing, 2003.
[29] C. Cortes, V. Vapnik, Support-vector networks, Mach. Learn. (1995).
[30] V. Vapnik, The Nature of Statistical Learning Theory, 1995. New York.
[31] J. Quinlan, Induction of decision trees, Mach. Learn. 1 (1986) 81e106.
[32] B. Pang, L. Lee, S. Vaithyanathan, Thumbs up?: sentiment classification
using machine learning techniques, in: Proc. Conf. Empirical Methods
Natural Language Processing, 2002, pp. 79e86.
[33] M. Gamon, Sentiment classification on customer feedback data: noisy data,
large feature vectors, and the role of linguistic analysis, in: Proceedings of
the 20th International Conference on Computational Linguistics, 2004.
[34] B. Pang, L. Lee, A sentimental education: sentiment analysis using
subjectivity summarization based on minimum cuts, in: 42nd Annual
Meeting of the Association for Computational Linguistics ACL, 2004,
pp. 271e280.