title: Discovering Fine-grained Sentiment in Suicide Notes.
abstract: This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.
authors.affiliation: Kno.e.sis Center, Wright State University, Dayton, USA.
authors.forename: Wenbo
authors.initials: W
authors.lastname: Wang
authors.forename: Lu
authors.initials: L
authors.lastname: Chen
authors.forename: Ming
authors.initials: M
authors.lastname: Tan
authors.forename: Shaojun
authors.initials: S
authors.lastname: Wang
authors.forename: Amit P
authors.initials: AP
authors.lastname: Sheth