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Words and phrases bespeak the perspectives of people about products, services, governments and events on social media. Extricating positive or negative polarities from social media text denominates task of sentiment analysis in the field of natural language processing. The exponential growth of demands for business organizations and governments, impel researchers to accomplish their research in sentiment analysis. This paper leverages four state-of-the-art machine learning classifiers viz. Naïve Bayes, J48, BFTree and OneR for optimization of sentiment analysis. The experiments are performed using three manually compiled datasets; two of them are captured from Amazon and one dataset is assembled from IMDB movie reviews. The efficacies of these four classification techniques are examined and compared. The Naïve Bayes found to be quite fast in learning whereas OneR seems more promising in generating the accuracy of 91.3% in precision, 97% in F-measure and 92.34% in correctly classified instances.

In general, the prevalence of psychiatric disorders among people with Hansen's disease has greatly increased to date. However, inadequate psychiatric care of people with Hansen's disease is an area of increasing concern. Many studies have been conducted in India and abroad to find out the prevalence of comorbid psychiatric disorders in patients suffering from Hansen's disease. Although efforts have been made by the government and international organizations to solve the medical problems among this group of patients, this disease still carries a number of psychosocial issues. The social stigma connected to these patients makes this disease completely different from others. Even nowadays people affected by Hansen's disease have to leave their village and are socially isolated. Depression is the most common psychiatric disorder found in these patients. Early detection and treatment of psychiatric disorders among Hansen's disease patients is a powerful psychotherapeutic measure. Integrated healthcare strategy will be beneficial to these patients. A comprehensive MEDLINE search and review of relevant literature was carried out and the data extracted and studied with particular reference to psychosocial issues in Hansen's disease. The focus of this research work is related to psychiatric and social aspects vis-à-vis stigma in these patients with Hansen's disease.

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This study suggests that caregivers of patients with schizophrenia experience higher stigma than the caregivers of patients with bipolar disorder and recurrent depressive disorder. Higher stigma is associated with higher psychological morbidity in the caregivers. Therefore, the clinicians managing patients with severe mental disorders must focus on stigma and psychological distress among the caregivers and plan intervention strategies to reduce stigma.