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        <title>A graphical framework to detect and categorize diverse opinions from online news</title>
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            <namePart type="given">Malvina</namePart>
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        <name type="personal">
            <namePart type="given">Viviana</namePart>
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    <abstract>This paper proposes a graphical framework to extract opinionated sentences which highlight different contexts within a given news article by introducing the concept of diversity in a graphical model for opinion detection. We conduct extensive evaluations and find that the proposed modification leads to impressive improvement in performance and makes the final results of the model much more usable. The proposed method (OP-D) not only performs much better than the other techniques used for opinion detection as well as introducing diversity, but is also able to select opinions from different categories (Asher et al. 2009). By developing a classification model which categorizes the identified sentences into various opinion categories, we find that OP-D is able to push opinions from different categories uniformly among the top opinions.</abstract>
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%0 Conference Proceedings
%T A graphical framework to detect and categorize diverse opinions from online news
%A Mullick, Ankan
%A Goyal, Pawan
%A Ganguly, Niloy
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%S Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F mullick-etal-2016-graphical
%X This paper proposes a graphical framework to extract opinionated sentences which highlight different contexts within a given news article by introducing the concept of diversity in a graphical model for opinion detection. We conduct extensive evaluations and find that the proposed modification leads to impressive improvement in performance and makes the final results of the model much more usable. The proposed method (OP-D) not only performs much better than the other techniques used for opinion detection as well as introducing diversity, but is also able to select opinions from different categories (Asher et al. 2009). By developing a classification model which categorizes the identified sentences into various opinion categories, we find that OP-D is able to push opinions from different categories uniformly among the top opinions.
%U https://aclanthology.org/W16-4305
%P 40-49

Markdown (Informal)

[A graphical framework to detect and categorize diverse opinions from online news](https://aclanthology.org/W16-4305) (Mullick et al., PEOPLES 2016)

• A graphical framework to detect and categorize diverse opinions from online news (Mullick et al., PEOPLES 2016)

ACL

• Ankan Mullick, Pawan Goyal, and Niloy Ganguly. 2016. A graphical framework to detect and categorize diverse opinions from online news. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 40–49, Osaka, Japan. The COLING 2016 Organizing Committee.