Citation

Desmet B, Hoste V. Expert Syst. Appl. 2013; 40(16): 6351-6358.

Copyright

(Copyright © 2013, Elsevier Publishing)

DOI

10.1016/j.eswa.2013.05.050

PMID

unavailable

Abstract

The success of suicide prevention, a major public health concern worldwide, hinges on adequate suicide risk assessment. Online platforms are increasingly used for expressing suicidal thoughts, but manual monitoring is unfeasible given the information overload experts are confronted with. We investigate whether the recent advances in natural language processing, and more specifically in sentiment mining, can be used to accurately pinpoint 15 different emotions, which might be indicative of suicidal behavior. A system for automatic emotion detection was built using binary support vector machine classifiers. We hypothesized that lexical and semantic features could be an adequate way to represent the data, as emotions seemed to be lexicalized consistently. The optimal feature combination for each of the different emotions was determined using bootstrap resampling. Spelling correction was applied to the input data, in order to reduce lexical variation. Classification performance varied between emotions, with scores up to 68.86% F-score. F-scores above 40% were achieved for six of the seven most frequent emotions: thankfulness, guilt, love, information, hopelessness and instructions. The most salient features are trigram and lemma bags-of-words and subjectivity clues. Spelling correction had a slightly positive effect on classification performance. We showed that fine-grained automatic emotion detection benefits from classifier optimization and a combined lexico-semantic feature representation. The modest performance improvements obtained through spelling correction might indicate the robustness of the system to noisy input text. We conclude that natural language processing techniques have future application potential for suicide prevention. © 2013 Elsevier B.V. All rights reserved.

Language: en

Keywords

Suicide; Risk assessment; Emotion; Semantics; Optimization; Natural language processing systems; Binary support vector machines; Classification performance; Feature representation; Information overloads; Natural language processing; NAtural language processing; Performance improvements