A Novel Two-stage Framework for Extracting Opinionated Sentences from News Articles

Abstract

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.

Understanding Language Preference for Expression of Opinion and Sentiment: What do Hindi-English Speakers do on Twitter?

“…Most of the existing research in opinion detection (Qadir, 2009;Brun, 2012;Rajkumar et al, 2014) and sentiment analysis (Mohammad, 2012;Mittal et al, 2013;Rosenthal et al, 2015) focus on monolingual tweets and sentences. Recently, there has been a couple of studies on sentiment detection of code-switched tweets (Vilares et al, 2015;Sharma et al, 2015b).…”