sielers : Feature Analysis and Polarity Classification of Expressions from Twitter and SMS Data
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چکیده
In this paper, we describe our system for the SemEval-2013 Task 2, Sentiment Analysis in Twitter. We formed features that take into account the context of the expression and take a supervised approach towards subjectivity and polarity classification. Experiments were performed on the features to find out whether they were more suited for subjectivity or polarity Classification. We tested our model for sentiment polarity classification on Twitter as well as SMS chat expressions, analyzed their F-measure scores and drew some interesting conclusions from them.
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تاریخ انتشار 2013