In this work, we analyze sentiments and opinions expressed in user-written Web messages. The messages discuss healthrelated topics: medications, treatment, illness and cure, etc. Recognition of sentiments and opinions is a challenging task for humans as well as an automated text analysis. In this work, we apply both the approaches. The paper presents the annotation model, discusses characteristics of subjectivity annotations in health-related messages, and reports the results of the annotation agreement. For external evaluation of the labeling results, we apply Machine Learning methods on the annotated data and present the obtained results.
['Marina Sokolova', 'Victoria Bobicev']
Sentiments and Opinions in Health-related Web messages