Predicting post severity in mental health forums

Shervin Malmasi, Marcos Zampieri, Mark Dras

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

We present our approach to predicting the severity of user posts in a mental health forum. This system was developed to compete in the 2016 Computational Linguistics and Clinical Psychology (CLPsych) Shared Task. Our entry employs a meta-classifier which uses a set of of base classifiers constructed from lexical, syntactic and metadata features. These classifiers were generated for both the target posts as well as their contexts, which included both preceding and subsequent posts. The output from these classifiers was used to train a meta-classifier, which outperformed all individual classifiers as well as an ensemble classifier. This meta-classifier was then extended to a Random Forest of meta-classifiers, yielding further improvements in classification accuracy. We achieved competitive results, ranking first among a total of 60 submitted entries in the competition.
Original languageEnglish
Title of host publicationProceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology
Subtitle of host publicationfrom linguistic signal to clinical reality
Place of PublicationStroudsburg, PA
PublisherAssociation for Computational Linguistics
Pages133-137
Number of pages5
ISBN (Print)9781941643549
Publication statusPublished - 2016
EventWorkshop on Computational Linguistics and Clinical Psychology (3rd : 2016): from linguistic signal to clinical reality - San Diego, United States
Duration: 16 Jun 201616 Jun 2016
Conference number: 3rd

Workshop

WorkshopWorkshop on Computational Linguistics and Clinical Psychology (3rd : 2016)
Abbreviated titleCLPsych, at NAACL 2016
CountryUnited States
CitySan Diego
Period16/06/1616/06/16

Fingerprint Dive into the research topics of 'Predicting post severity in mental health forums'. Together they form a unique fingerprint.

Cite this