research-article

Unsupervised stance classification in online debates

Authors:

Konjengbam Anand,

Sailaja Rajanala,

A Bharath Reddy,

Manish SinghAuthors Info & Claims

CODS-COMAD '18: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data

Pages 30 - 36

https://doi.org/10.1145/3152494.3152497

Published: 11 January 2018 Publication History

Abstract

This paper proposes an unsupervised debate stance classification algorithm. In other words, finding the side a post author is taking in an online debate. Stance detection has a complementary role in information retrieval, opinion mining, text summarization, etc. Existing stance detection techniques are not able to effectively handle two challenges: determine whether a given post is a debate or not? If the post is a debate on a given topic, correctly classify the side that the post author is taking. In this paper, we propose techniques that addresses both the above issues. Compared to existing technique, our technique gives 30% improvement in detection of whether a post is a debate or not. Our technique is able to find the side that an author is taking in a debate by 10% higher F1 score compared to existing work. We achieve this improvement by using new syntactic rules, better aspect popularity detection, co-reference resolution, and a novel integer linear programming model to solve the problem.

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Cited By

Saha RLakshmanan LNg R(2023)Stance Detection with ExplanationsComputational Linguistics10.1162/coli_a_0050150:1(193-235)Online publication date: 1-Mar-2023
https://doi.org/10.1162/coli_a_00501
Menon AAnand K(2023)X-ABI: Toward Parameter-Efficient Multilingual Adapter-Based Inference for Cross-Lingual TransferData Management, Analytics and Innovation10.1007/978-981-99-1414-2_23(303-317)Online publication date: 29-May-2023
https://doi.org/10.1007/978-981-99-1414-2_23
Jin LXu KSong LYu D(2021)Distant Finetuning with Discourse Relations for Stance ClassificationNatural Language Processing and Chinese Computing10.1007/978-3-030-88483-3_39(484-495)Online publication date: 6-Oct-2021
https://doi.org/10.1007/978-3-030-88483-3_39
Show More Cited By

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Published In

cover image ACM Other conferences

CODS-COMAD '18: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data

January 2018

379 pages

ISBN:9781450363419

DOI:10.1145/3152494

Conference Chair:
Sayan Ranu
IIT Delhi
,
General Chairs:
Niloy Ganguly
IIT Kharagpur
,
Raghu Ramakrishnan
Microsoft
,
Program Chairs:
Sunita Sarawagi
IIT Bombay
,
Shourya Roy
American Express Big Data Labs

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 January 2018

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CoDS-COMAD '18

CoDS-COMAD '18: The ACM India Joint International Conference on Data Science & Management of Data

January 11 - 13, 2018

Goa, India

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CODS-COMAD '18 Paper Acceptance Rate 50 of 150 submissions, 33%;

Overall Acceptance Rate 197 of 680 submissions, 29%

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Citations

Cited By

Saha RLakshmanan LNg R(2023)Stance Detection with ExplanationsComputational Linguistics10.1162/coli_a_0050150:1(193-235)Online publication date: 1-Mar-2023
https://doi.org/10.1162/coli_a_00501
Menon AAnand K(2023)X-ABI: Toward Parameter-Efficient Multilingual Adapter-Based Inference for Cross-Lingual TransferData Management, Analytics and Innovation10.1007/978-981-99-1414-2_23(303-317)Online publication date: 29-May-2023
https://doi.org/10.1007/978-981-99-1414-2_23
Jin LXu KSong LYu D(2021)Distant Finetuning with Discourse Relations for Stance ClassificationNatural Language Processing and Chinese Computing10.1007/978-3-030-88483-3_39(484-495)Online publication date: 6-Oct-2021
https://doi.org/10.1007/978-3-030-88483-3_39
Küçük DCan F(2020)Stance DetectionACM Computing Surveys10.1145/336902653:1(1-37)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3369026
Wang RZhou DJiang MSi JYang Y(2019)A Survey on Opinion Mining: From Stance to Product AspectIEEE Access10.1109/ACCESS.2019.29067547(41101-41124)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2906754
Konjengbam AGhosh SKumar NSingh M(2018)Debate Stance Classification Using Word EmbeddingsBig Data Analytics and Knowledge Discovery10.1007/978-3-319-98539-8_29(382-395)Online publication date: 8-Aug-2018
https://doi.org/10.1007/978-3-319-98539-8_29

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