Fuzzy Based Implicit Sentiment Analysis on Quantitative Sentences
With the rapid growth of social media on the web, emotional polarity computation has become a flourishing frontier in the text mining community. However, it is challenging to understand the latest trends and summarize the state or general opinions about products due to the big diversity and size of social media data and this creates the need of automated and real time opinion extraction and mining. On the other hand, the bulk of current research has been devoted to study the subjective sentences which contain opinion keywords and limited work has been reported for objective statements that imply sentiment. In this paper, fuzzy based knowledge engineering model has been developed for sentiment classification of special group of such sentences including the change or deviation from desired range or value. Drug reviews are the rich source of such statements. Therefore, in this research, some experiments were carried out on patientâ€™s reviews on several different cholesterol lowering drugs to determine their sentiment polarity. The main conclusion through this study is, in order to increase the accuracy level of existing drug opinion mining systems, objective sentences which imply opinion should be taken into account.
Abacha, A. B., & Zweigenbaum, P. (2011). Automatic extraction of semantic relations between medical entities: a rule based approach. Journal of biomedical semantics, 2(Suppl 5), S4.
Abbod, M. F., von Keyserlingk, D. G., Linkens, D. A., & Mahfouf, M. (2001). Survey of utilisation of fuzzy technology in medicine and healthcare. Fuzzy Sets and Systems, 120(2), 331-349.
Bhatia, R. S., Graystone, A., Davies, R. A., McClinton, S., Morin, J., & Davies, R. F. (2010). Extracting information for generating a diabetes report card from free text in physicians notes. Paper presented at the Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents.
Cao, H., Hripcsak, G., & Markatou, M. (2007). A statistical methodology for analyzing co-occurrence data from a large sample. Journal of biomedical informatics, 40(3), 343-352.
Capuzzi, D. M., Guyton, J. R., Morgan, J. M., Goldberg, A. C., Kreisberg, R. A., Brusco, O., & Brody, J. (1998). Efficacy and safety of an extended-release niacin (Niaspan): a long-term study. The American journal of cardiology, 82(12), 74U-81U.
Cunningham, H. (2002). GATE, a general architecture for text engineering. Computers and the Humanities, 36(2), 223-254.
Ding, X., Liu, B., & Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining. Paper presented at the Proceedings of the 2008 International Conference on Web Search and Data Mining.
Hatiboglu, M. A., Altunkaynak, A., Ozger, M., Iplikcioglu, A. C., Cosar, M., & Turgut, N. (2010). A predictive tool by fuzzy logic for outcome of patients with intracranial aneurysm. Expert Systems with Applications, 37(2), 1043-1049.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Paper presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining.
Jones, P. H., Davidson, M. H., Stein, E. A., Bays, H. E., McKenney, J. M., Miller, E., . . . Blasetto, J. W. (2003). Comparison of the efficacy and safety of< i> rosuvastatin versus< i> atorvastatin,< i> simvastatin, and< i> pravastatin across doses (STELLAR Trial). The American journal of cardiology, 92(2), 152-160.
KeleÅŸ, A., KeleÅŸ, A., & Yavuz, U. (2011). Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Expert Systems with Applications, 38(5), 5719-5726.
Kim, S.-M., & Hovy, E. (2004). Determining the sentiment of opinions. Paper presented at the Proceedings of the 20th international conference on Computational Linguistics.
Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2, 627-666.
Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis Mining Text Data (pp. 415-463): Springer.
Mamdani, E. H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Paper presented at the Proceedings of the Institution of Electrical Engineers.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2), 1-135.
Rosier, A., Burgun, A., & Mabo, P. (2008). Using regular expressions to extract information on pacemaker implantation procedures from clinical reports. Paper presented at the AMIA Annual Symposium Proceedings.
Ross, T. J. (2009). Fuzzy logic with engineering applications: John Wiley & Sons.
Rushdi Saleh, M., MartÃn-Valdivia, M. T., Montejo-RÃ¡ez, A., & UreÃ±a-LÃ³pez, L. (2011). Experiments with SVM to classify opinions in different domains. Expert Systems with Applications, 38(12), 14799-14804.
Samuel, O., Omisore, M., & Ojokoh, B. (2013). A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever. Expert Systems with Applications, 40(10), 4164-4171.
Simpson, M. S., & Demner-Fushman, D. (2012). Biomedical text mining: A survey of recent progress Mining Text Data (pp. 465-517): Springer.
Swaminathan, R., Sharma, A., & Yang, H. (2010). Opinion mining for biomedical text data: Feature space design and feature selection. Paper presented at the The Nineth International Workshop on Data Mining in Bioinformatics, BIOKDD.
Turney, P. D. (2002). Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. Paper presented at the Proceedings of the 40th annual meeting on association for computational linguistics.
Yalamanchi, D. (2011). Sideffective-system to mine patient reviews: sentiment analysis. Rutgers University-Graduate School-New Brunswick.
Yu, H., & Hatzivassiloglou, V. (2003). Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. Paper presented at the Proceedings of the 2003 conference on Empirical methods in natural language processing.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Zhang, L., & Liu, B. (2011). Identifying noun product features that imply opinions. Paper presented at the Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2.
Zhou, X., Han, H., Chankai, I., Prestrud, A., & Brooks, A. (2006). Approaches to text mining for clinical medical records. Paper presented at the Proceedings of the 2006 ACM symposium on Applied computing.
Zhou, X., Han, H., Chankai, I., Prestrud, A. A., & Brooks, A. D. (2005). Converting semi-structured clinical medical records into information and knowledge. Paper presented at the Data Engineering Workshops, 2005. 21st International Conference on.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © 2014 Penerbit UTM Press. Universiti Teknologi Malaysia. All rights reserved.
Mailing Address: Penerbit UTM Press, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.