@article{ref1,
title="Discovering fine-grained sentiment in suicide notes",
journal="Biomedical informatics insights",
year="2012",
author="Wang, Wenbo and Chen, Lu and Tan, Ming and Wang, Shaojun and Sheth, Amit P.",
volume="5",
number="Suppl 1",
pages="137-145",
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.<p /> <p>Language: en</p>",
language="en",
issn="1178-2226",
doi="10.4137/BII.S8963",
url="http://dx.doi.org/10.4137/BII.S8963"
}