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.
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