Sentiment Analysis using Partial Textual Entailment

The internet is an ocean of raw data in the form of social text messages, tweets, blogs etc. - arbitrary and unconnected (at the first glance). Only after processing, organizing and structuring of online text snippets, the data becomes information. From the Literature survey, we observed that the present sentiment analysis tools are slow, bulky and computationally heavy which makes the task at hand inefficient. Therefore, to overcome the aforementioned problem of analyzing sentiments efficiently, a new method is proposed in the present paper to drive the task of Sentiment Analysis by exploiting the idea of Partial Textual Entailment. We propose to use Partial Textual Entailment for measuring semantic similarity between the tweets so as to group similar tweets together. The method is anticipated to reduce the burden of sentiment analyzer and makes the processing faster. Moreover, we also propose a modification in an existing method of Partial Textual Entailment which can be further adopted for many Natural Language Processing applications.