WordNet: a lexical database for English
Communications of the ACM
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Investigating web search strategies and forum use to support diet and weight loss
CHI '09 Extended Abstracts on Human Factors in Computing Systems
How valuable is medical social media data? Content analysis of the medical web
Information Sciences: an International Journal
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Improving Patient Opinion Mining through Multi-step Classification
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Building domain-oriented sentiment lexicon by improved information bottleneck
Proceedings of the 18th ACM conference on Information and knowledge management
Proceedings of the third ACM international conference on Web search and data mining
The viability of web-derived polarity lexicons
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Analysis of an online health social network
Proceedings of the 1st ACM International Health Informatics Symposium
Automatic integration of drug indications from multiple health resources
Proceedings of the 1st ACM International Health Informatics Symposium
Aspect-based sentiment analysis of movie reviews on discussion boards
Journal of Information Science
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Opinion mining consists in extracting from a text opinions expressed by its author and their polarity. Lexical resources, such as polarized lexicons, are needed for this task. Opinion mining in the medical domain has not been well explored, partly because little credence is given to patients and their opinions (although more and more of them are using social media). We are interested in opinion mining of user-generated content on drugs/medication. We present in this paper the creation of our lexical resources and their adaptation to the medical domain. We first describe the creation of a general lexicon, containing opinion words from the general domain and their polarity. Then we present the creation of a medical opinion lexicon, based on a corpus of drug reviews. We show that some words have a different polarity in the general domain and in the medical one. Some words considered generally as neutral are opinionated in medical texts. We finally evaluate the lexicons and show with a simple algorithm that using our general lexicon gives better results than other well-known ones on our corpus and that adding the domain lexicon improves them as well.