Using natural language processing to classify suicide notes
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Clustering semantic spaces of suicide notes and newsgroups articles
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Syntactic complexity measures for detecting mild cognitive impairment
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Reading the markets: forecasting public opinion of political candidates by news analysis
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Mining association language patterns for negative life event classification
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Natural Language Processing with Python
Natural Language Processing with Python
An analysis of verbs in financial news articles and their impact on stock price
WSA '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media
Characteristics of high agreement affect annotation in text
LAW IV '10 Proceedings of the Fourth Linguistic Annotation Workshop
Cancer stage prediction based on patient online discourse
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Python Text Processing with NLTK 2.0 Cookbook
Python Text Processing with NLTK 2.0 Cookbook
Learning python, fourth edition
Learning python, fourth edition
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Improving mental wellness with preventive measures can help people at risk of experiencing mental health conditions such as depression or post-traumatic stress disorder. We describe an encouraging study on how automatic analysis of short written texts based on relevant linguistic text features can be used to identify whether the authors of such texts are experiencing distress. Such a computational model can be useful in developing an early warning system able to analyze writing samples for signs of mental distress. This could serve as a red flag, signaling when someone might need a professional assessment by a clinician. This paper reports on classification of distressed and non-distressed short, written excerpts from relevant web forums, using features automatically extracted from input text. Varying the value of k in k-fold cross-validation shows that both coarse-grained and fine-grained automatic classification of affect states are generally 20% more accurate in detecting affect state than randomly assigning a distress label to a text. The study also compares the importance of bundled linguistic super-factors with a 2k factorial model. Analyzing the importance of different linguistic features for this task indicates main effects of affect word list matches, pronouns, and parts of speech in the predictive model. Excerpt length contributed to interaction effects.