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abstract

Mining Twitter for Fine-Grained Political Opinion Polarity Classification, Ideology Detection and Sarcasm Detection

Published:02 February 2018Publication History

ABSTRACT

In this paper, we propose three models for socio-political opinion polarity classification of microblog posts. Firstly, a novel probabilistic model, Joint-Entity-Sentiment-Topic (JEST) model, which captures opinions as a combination of the target entity, sentiment and topic, will be proposed. Secondly, a model for ideology detection called JEST-Ideology will be proposed to identify an individual»s orientation towards topics/issues and target entities by extending the proposed opinion polarity classification framework. Finally, we propose a novel method to accurately detect sarcastic opinions by utilizing detected fine-grained opinion and ideology.

References

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  1. Mining Twitter for Fine-Grained Political Opinion Polarity Classification, Ideology Detection and Sarcasm Detection

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      • Published in

        cover image ACM Conferences
        WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining
        February 2018
        821 pages
        ISBN:9781450355810
        DOI:10.1145/3159652

        Copyright © 2018 Owner/Author

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 2 February 2018

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        Acceptance Rates

        WSDM '18 Paper Acceptance Rate81of514submissions,16%Overall Acceptance Rate498of2,863submissions,17%

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