Discovering Fine-grained Sentiment in Suicide Notes
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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Published in Biomedical Informatics Insights, Volume 5, Issue 1, 2012.
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Wang, W., Chen, L., Tan, M., Wang, S., & Sheth, A. P. (2012). Discovering Fine-Grained Sentiment in Suicide Notes. Biomedical Informatics Insights, 5, 137-145.