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        <title>Investigating Political Herd Mentality: A Community Sentiment Based Approach</title>
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        <namePart type="given">Anjali</namePart>
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        <namePart type="family">Mishra</namePart>
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        <namePart type="given">Ramit</namePart>
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    <abstract>Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today. This experiment aims to address this issue by analyzing publicly-available Hansard transcripts of the debates conducted in the UK Parliament. Our proposed approach, which uses community-based graph information to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts, currently surpasses the benchmark results on the same dataset. Such sentiment classification systems could prove to be of great use in today’s politically turbulent times, for public knowledge of politicians’ stands on various relevant issues proves vital for good governance and citizenship. The experiments also demonstrate that continuous feature representations learned from graphs can improve performance on sentiment classification tasks significantly.</abstract>
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    <identifier type="doi">10.18653/v1/P19-2039</identifier>
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%0 Conference Proceedings
%T Investigating Political Herd Mentality: A Community Sentiment Based Approach
%A Bhavan, Anjali
%A Mishra, Rohan
%A Sinha, Pradyumna Prakhar
%A Sawhney, Ramit
%A Shah, Rajiv Ratn
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F bhavan-etal-2019-investigating
%X Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today. This experiment aims to address this issue by analyzing publicly-available Hansard transcripts of the debates conducted in the UK Parliament. Our proposed approach, which uses community-based graph information to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts, currently surpasses the benchmark results on the same dataset. Such sentiment classification systems could prove to be of great use in today’s politically turbulent times, for public knowledge of politicians’ stands on various relevant issues proves vital for good governance and citizenship. The experiments also demonstrate that continuous feature representations learned from graphs can improve performance on sentiment classification tasks significantly.
%R 10.18653/v1/P19-2039
%U https://aclanthology.org/P19-2039
%U https://doi.org/10.18653/v1/P19-2039
%P 281-287

Markdown (Informal)

[Investigating Political Herd Mentality: A Community Sentiment Based Approach](https://aclanthology.org/P19-2039) (Bhavan et al., ACL 2019)

• Investigating Political Herd Mentality: A Community Sentiment Based Approach (Bhavan et al., ACL 2019)

ACL

• Anjali Bhavan, Rohan Mishra, Pradyumna Prakhar Sinha, Ramit Sawhney, and Rajiv Ratn Shah. 2019. Investigating Political Herd Mentality: A Community Sentiment Based Approach. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 281–287, Florence, Italy. Association for Computational Linguistics.