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Discovering Fine-grained Sentiment in Suicide Notes

Title Discovering Fine-grained Sentiment in Suicide Notes
Author , , , ,
Book Biomedical Informatics Insights
Publisher Biomedical Informatics Insights
Year 2012
Resource Type Journal Article
Keyword(s) Sentiment Analysis,Suicide Note,Emotion Identification
Pages 137-145
Full Citation Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P. Sheth. Discovering Fine-grained Sentiment in Suicide Notes. Biomedical Informatics Insights, vol. 5 (Suppl. 1) pp. 137-145, 2012
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.
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