Final Words: Topic Detection in Suicide Notes

The data set for this year's i2b2 Natural Language Processing Challenge was an unusual one. 900 suicide notes, annotated with 15 topics (13 emotions and 2 speech acts): abuse, anger, blame, fear, forgiveness, guilt, happiness or peacefulness, hopefulness, hopelessness, information, instructions, love, pride, sorrow and thankfulness. The objective of the challenge was to accurately predict their presence at the sentence level. We developed a system that uses 15 SVM models (1 per topic), using the combination of features that was found to perform best on a given topic. The features represented lexical and semantic information: lemma and trigram bag of words, and features using WordNet, SentiWordNet and a list of subjectivity clues. SVM results were improved by changing the classification threshold, using bootstrap resampling to prevent overfitting. We discuss the usability of this approach for emotion classification, and present the results