Identifying the sentiment of the text has recently gained a lot of popularity probably due to availability of huge datasets, especially on social networking sites of internet. The social networking sites like twitter and facebook provides to people in general the effective platform for expression of their thoughts and ideas. These thoughts can be harnessed for extraction of sentiments of people related to various issues. But since expression of the verbal thoughts differs individually, identifying the right sentiment from bulk of data becomes the real challenge. In this paper we suggest an approach to analyze the sentiment of the text available on social media.
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Kasture et al. (2015) studied this question.
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