Sentiment Lexicon Construction Using SentiWordNet 3.0

Opinion mining and sentiment analysis have become popular in linguistic resource rich languages. Opinions for such analysis are drawn from many forms of freely available online/electronic sources, such as websites, blogs, news re-ports and product reviews. But attention received by less resourced languages is significantly less. This is because the success of any opinion mining algorithm depends on the availability of resources, such as special lexicon and WordNet type tools. In this research, we implemented a less complicated but an effective approach that could be used to classify comments in less resourced languages. We experimented the approach for use with Sinhala Language where no such opinion mining or sentiment analysis has been carried out until this day. Our algorithm gives significantly promising results for analyzing sentiments in Sinhala for the first time

A Comparative Analysis of Opinion Mining and Sentiment Classification in Non-english Languages

In the past decade many opinion mining and sentiment classification studies have been carried out for opinions in English. However, the amount of work done for non-English text opinions is very limited.In this review, we investigate opinion mining and sentiment classification studies in three non-English languages to find the classification methods and the efficiency of each algorithm used in these methods. It is found that most of the research conducted for non-English has followed the methods used in the English language with onlylimited usage of language specific properties, such as morphological variations. The application domains seem to be restricted to particular fields and significantly less research has been conducted in cross domains. Keywords—Natural Language processing, Text mining, Machine Learning

The burden of diabetes mellitus and impaired fasting glucose in an urban population of Sri Lanka

Aims To describe the burden of diabetes mellitus and impaired fasting glucose in middle-aged residents (35–64 years) in an urban area of Sri Lanka. Methods A cross-sectional survey was conducted in the Ragama Medical Officer of Health area, from which 2986 participants (1349 men and 1637 women) were randomly selected from the electoral registry between January and December 2007. The participants underwent a physical examination and had their height, weight, waist and hip circumferences and blood pressure measured by trained personnel. Fasting blood samples were taken for measurement of glucose, HbA1c and lipids. The prevalence of diabetes (fasting plasma glucose > 7 mmol/l) and impaired fasting glycaemia (fasting plasma glucose 5.6–6.9 mmol/l) and major predictors of diabetes in Sri Lanka were estimated from the population-based data. Results Age-adjusted prevalence of diabetes mellitus in this urban population was 20.3% in men and 19.8% in women. Through the present screening, 263 patients with diabetes and 1262 with impaired fasting glucose levels were identified. The prevalence of newly detected diabetes was 35.7% of all patients with diabetes. Among patients with diabetes, only 23.8% were optimally controlled. In the regression models, high BMI, high waist circumference, high blood pressure and hypercholesterolaemia increased the fasting plasma glucose concentration, independent of age, sex and a family history of diabetes. Conclusions Our data demonstrate the heavy burden of diabetes in this urban population. Short- and long-term control strategies are required, not only for optimal therapy among those affected, but also for nationwide primary prevention of diabetes