Textual prediction of attitudes towards mental health. Issue 3 (2015)

• Record Type: Journal Article
• Authors: Patrick, Mensah Kwabena
• Is Part Of: International journal of knowledge engineering and data mining. Volume 3:Issue 3/4(2015)
• Page Start: 274
• Page End: 285
• Publication Date: 2015
• Subjects: mental health -- classification -- sentiment analysis -- textual prediction -- machine learning -- support vector machines -- SVM -- regression forest -- maximum entropy -- Ghana -- text mining -- text analysis -- behaviour prediction; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

Abstract

A simple search on Google with the phrase 'Mental Health in Ghana' will produce lots of hits including news items through publications to mental health bills. A lot of information is hidden in such unstructured data and could be mined to improve mental health in Ghana. For this reason, lexicon-based sentiment analysis and supervised machine learning was applied on a corpus of 28 journal articles and two news items in mental health. The sentiment score for the lexicon-based text analysis was negative indicating negative impression on mental health. SVM classification outperformed other algorithms with accuracy of 0.839. MAXENT was the worst performer (accuracy 0.774). F-score for SVM was 0.67; 0.64 for MAXENT and 0.59 for RF. 83.9% accuracy means that we can effectively predict human behaviour towards mental health via text mining because our behaviours are influenced by opinions we carry.