Background – Social distancing leads to decrease in the spread of the novel virus but at the same time it shows to have a negative effect on the quality of life of the general population. Methodology - A cross-sectional survey study was conducted using an electronic version of WHOQOL-BREF scale. The demographic data was collected along with the 26 questions of the scale. We distributed this survey to the general population through electronic and social media. Results – We received 861 responses. Excluding the incomplete responses, we analyzed 832 responses. They had a mean age of 48.33yrs, majority being graduates (56.4%), majority of the subjects were males (59.25%), and there was no significant age difference between both the genders. The overall quality of life was perceived to be 3.48 and the satisfaction for health was 3.77. Most affected domains were the physical and psychological domains. Lowest quality of life responses were noted for questions pertaining to financial, transportation and sleep related. Conclusion- The quality of life in lock downs due to corona virus is affected due to social distancing. Law makers need to take care to avoid increasing this negative impact while enforcing lock downs.
This paper presents a novel two-stage framework to extract opinionated sentences from a given news article. In the first stage, Naïve Bayes classifier by utilizing the local features assigns a score to each sentence -the score signifies the probability of the sentence to be opinionated. In the second stage, we use this prior within the HITS (Hyperlink-Induced Topic Search) schema to exploit the global structure of the article and relation between the sentences. In the HITS schema, the opinionated sentences are treated as Hubs and the facts around these opinions are treated as the Authorities. The algorithm is implemented and evaluated against a set of manually marked data. We show that using HITS significantly improves the precision over the baseline Naïve Bayes classifier. We also argue that the proposed method actually discovers the underlying structure of the article, thus extracting various opinions, grouped with supporting facts as well as other supporting opinions from the article.
This paper presents a novel two-stage framework to extract opinionated sentences from a given news article. In the first stage, Naïve Bayes classifier by utilizing the local features assigns a score to each sentence -the score signifies the probability of the sentence to be opinionated. In the second stage, we use this prior within the HITS (Hyperlink-Induced Topic Search) schema to exploit the global structure of the article and relation between the sentences. In the HITS schema, the opinionated sentences are treated as Hubs and the facts around these opinions are treated as the Authorities. The algorithm is implemented and evaluated against a set of manually marked data. We show that using HITS significantly improves the precision over the baseline Naïve Bayes classifier. We also argue that the proposed method actually discovers the underlying structure of the article, thus extracting various opinions, grouped with supporting facts as well as other supporting opinions from the article. Related WorkOpinion mining has drawn a lot of attention in recent years. Research works have focused on mining
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