1 |
Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M. (1966). General Inquirer: a computer approach to content analysis. Cambridge, MA: MIT Press.
|
2 |
Wilson, T., Wiebe, J., & Hoffmann, P. (2009). Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics, 35(3), 399-433.
DOI
|
3 |
Xia, L., Gentile, A. L., Munro, J., & Iria, J. (2009). Improving patient opinion mining through multistep classification. Lectures Notes in Artificial Intelligence, 5729, 70-76.
|
4 |
Sarasohn-Kahn, J. (2008). The wisdom of patients: Health care meets online social media. California Healthcare Foundation, Oakland. Retrieved from http://www.chcf.org/publications/2008/04/the-wisdom-of-patients-health-care-meets-online-social-media.
|
5 |
Schraefel, M. C., White, R. W., André, P., & Tan, D. (2009). Investigating Web search strategies and forum use to support diet and weight loss. Proceedings of the 27th international conference extended abstracts on Human factors in computing systems (CHI ‘09) (pp. 3829-3834).
|
6 |
Shaikh, M. A. M., Prendinger, H., & Ishizuka, M. (2008). Sentiment assessment of text by analyzing linguistic features and contextual valence assignment. Applied Artificial Intelligence, 22(6), 558-601.
DOI
ScienceOn
|
7 |
Tsytsarau, M., Palpanas, T., & Denecke, K. (2011). Scalable detection of sentiment-based contradictions. Proceedings of the First International Workshop on Knowledge Diversity on the Web.
|
8 |
Wiebe, J., & Riloff, E. (2005). Creating subjective and objective sentence classifiers from unannotated texts. Proceedings of Sixth International Conference on Intelligent Text Processing and Computational Linguistics (CICLing) (pp. 486-497).
|
9 |
Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (pp. 347-354).
|
10 |
Niu, Y., Zhu, X., Li, J., & Hirst, G. (2005). Analysis of polarity information in medical text. Proceedings of the American Medical Informatics Association Symposium (AMIA) (pp. 570-574).
|
11 |
Quirk, R., Greenbaum, S., Leech, G., & Svartvik, J. (1985). A comprehensive grammar of the English language. London: Longman.
|
12 |
Na, J.-C., Kyaing, W. Y. M., Khoo, C., Foo, S., Chang, Y.-K., and Theng, Y.-L. (2012). Sentiment classification of drug reviews using a rule-based linguistic approach. Proceedings of ICADL (International Conference on Asian Digital Libraries) ‘2012 (pp. 189-198). Taipei, Taiwan.
|
13 |
Nikfarjam, A., & Gonzalez, G. H. (2011). Pattern mining for extraction of mentions of adverse drug reactions from user comments. Proceedings of AMIA Annual Symposium (pp. 1019-1026).
|
14 |
Palmer, F. R. (2001). Mood modality. 2nd Edition. New York: Cambridge University Press.
|
15 |
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
DOI
|
16 |
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine-learning techniques. Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (pp. 79-86).
|
17 |
Lu, Y., Castellanos, M., Dayal, U., & Zhai, C. X. (2011). Automatic construction of a context-aware sentiment lexicon: An optimization approach. Proceedings of the 20th International Conference on World Wide Web (WWW 2011) (pp. 347-356). Hyderabad, India.
|
18 |
Polanyi, L., & Zaenen, A. (2006). Contextual valence shifters. In J. G. Shanahan, Y. Qu, & J. Wiebe (Eds.), Computing attitude and affect in text: Theory and applications (pp. 1-10), Information Retrieval Series Volume 20. Dordrecht: Springer Netherlands.
|
19 |
Qiu, G., Liu, B., Bu, J., & Chen, C. (2009). Expanding domain sentiment lexicon through double propagation. Proceedings of the 21st International Joint Conference on Artificial Intelligence (pp. 1199-1204). San Francisco: Morgan Kaufmann.
|
20 |
Ding, X., Liu, B., & Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining. Proceedings of the International Conference on Web Search and Web Data Mining (pp. 231-240). New York: ACM.
|
21 |
de Marneffe, M.-C., MacCartney, B., & Manning, C. D. (2006). Generating typed dependency parses from phrase structure parses. Proceedings of the 5th International Conference on Language Resources and Evaluation (pp. 449-454).
|
22 |
Denecke, K. (2008). Accessing medical experiences and information. Proceedings of the Workshop on Mining Social Data, European Conference on Artificial Intelligence.
|
23 |
Huang, S., Niu, Z., & Shi, C. (2014). Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowledge-Based Systems, 56, 191-200.
DOI
ScienceOn
|
24 |
Jaloba, A. (2009). The club no one wants to join: Online behaviour on a breast cancer discussion forum. First Monday, 14(7).
|
25 |
Bodenreider, O., & McCray, A. T. (2003). Exploring semantic groups through visual approaches. Journal of Biomedical Informatics, 36, 414-432.
DOI
ScienceOn
|
26 |
Jo, Y., & Oh, A. H. (2011). Aspect and sentiment unification model for online review analysis. Proceedings of the Fourth International Conference on Web Search and Data Mining (WSDM) (pp. 815-824). Hong Kong.
|
27 |
Kim, S., Zhang, J., Chen, Z., Oh, A., & Liu S. (2013). A hierarchical aspect-sentiment model for online reviews. Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (pp. 526-533).
|
28 |
Liu, B. (2012). Sentiment analysis and opinion mining. San Rafael, CA: Morgan & Claypool.
|
29 |
Aronson, A. R., & Lang, F. M. (2010). An overview of MetaMap: Historical perspective and recent advances. Journal of American Medical Informatics Association (JAMIA), 17, 229-236.
DOI
ScienceOn
|
30 |
Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010) (pp. 2200–2204).
|