Discovering Fine-grained Sentiment in Suicide Notes
Document Type
Article
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.
Digital Object Identifier (DOI)
Publication Info
Published in Biomedical Informatics Insights, Volume 5, Issue 1, 2012.
This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
APA Citation
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.
https://doi.org/10.4137%2FBII.S8963