Sentiment lexicons for health-related opinion mining

Abstract

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.