What Goes Around Comes Around: Learning Sentiments in Online Medical Forums

Currently 19%-28% of Internet users participate in online health discussions. A 2011 survey of the US population estimated that 59% of all adults have looked online for information about health topics such as a specific disease or treatment. Although empirical evidence strongly supports the importance of emotions in health-related messages, there are few studies of the relationship between a subjective lan-guage and online discussions of personal health. In this work, we study sentiments expressed on online medical forums. As well as considering the predominant sentiments expressed in individual posts, we analyze sequences of sentiments in online discussions. Individual posts are classified into one of five categories. We identified three categories as sentimental (encouragement, gratitude, confusion) and two categories as neutral (facts, endorsement). 1438 messages from 130 threads were annotated manually by two annotators with a strong inter-annotator agreement (Fleiss kappa = 0.737 and 0.763 for posts in se-quence and separate posts respectively). The annotated posts were used to analyse sentiments in consec-utive posts. In four multi-class classification problems, we assessed HealthAffect, a domain-specific af-fective lexicon, as well general sentiment lexicons in their ability to represent messages in sentiment recognition

Relatório de estágio em farmácia comunitária

Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr

Sentiment and Factual Transitions in Online Medical Forums

This work studies sentiment and factual transitions on an online medical forum where users correspond in English. We work with discussions dedicated to reproductive technologies, an emotionally-charged issue. In several learning problems, we demonstrate that multi-class sentiment classification significantly improves when messages are represented by affective terms combined with sentiment and factual transition information (paired t-test, P=0.0011).Self-funde

Universal Dependencies 2.1

LINDAT/CLARIN digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University - Corpus - Project code: 15-10472S; Project name: Morphologically and Syntactically Annotated Corpora of Many LanguagesUniversal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).http://hdl.handle.net/11234/1-251