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Stance Detection in Hindi-English Code-Mixed Data

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Vivek SrivastavaAuthors Info & Claims

CoDS COMAD 2020: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD

Pages 359 - 360

https://doi.org/10.1145/3371158.3371226

Published: 15 January 2020 Publication History

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Abstract

Social media sites such as Twitter, Facebook, and many other microblogging forums have emerged as a platform for people to express their opinions and perspectives on different events. People often tend to take a stance; in favor, against or neutral towards a particular topic on these platforms. Hindi and English are the most widely used languages on social media platforms in India, and the user predominantly expresses their opinions in Hindi-English code-mixed texts. As a result, knowing the diverse opinions of the masses is difficult. We target to classify Hindi-English code-mixed tweets based on their stance. A dataset consisting of 3545 English-Hindi code-mixed tweets with Demonetisation in the target is used in the experiments so far. We present a new stance annotated dataset of English-Hindi 4219 code-mixed tweets with the abrogation of article 370 in focus.

References

[1]

Irshad Bhat, Riyaz A Bhat, Manish Shrivastava, and Dipti Sharma. 2017. Joining Hands: Exploiting Monolingual Treebanks for Parsing of Code-mixing Data. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Vol. 2. 324--330.

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[2]

Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava, and Dipti Misra Sharma. 2018. Universal Dependency Parsing for Hindi-English Code-switching. CoRR abs/1804.05868 (2018). arXiv:1804.05868 http://arxiv.org/abs/1804.05868

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[3]

Sushmitha Reddy Sane, Suraj Tripathi, Koushik Reddy Sane, and Radhika Mamidi. 2019. Stance Detection in Code-Mixed Hindi-English Social Media Data using Multi-Task Learning. In Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 1--5.

Google Scholar

[4]

Sahil Swami, Ankush Khandelwal, Vinay Singh, Syed Sarfaraz Akhtar, and Manish Shrivastava. 2018. An English-Hindi Code-Mixed Corpus: Stance Annotation and Baseline System. CoRR abs/1805.11868 (2018). arXiv:1805.11868 http://arxiv.org/abs/1805.11868

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Cited By

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Ghafouri ANaderi HFirouzmandi M(2024)PinLID: a dataset for Pinglish language identiftcation based on code-mixing sentence on unstructured resourcesLanguage Resources and Evaluation10.1007/s10579-024-09783-3Online publication date: 7-Dec-2024
https://doi.org/10.1007/s10579-024-09783-3
Bhalla AChadha A(2023)An Efficient Model to Detect the Presence of Hinglish Text in YouTube Data2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)10.1109/ICAICCIT60255.2023.10465821(385-391)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICAICCIT60255.2023.10465821

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Published In

CoDS COMAD 2020: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD

January 2020

399 pages

ISBN:9781450377386

DOI:10.1145/3371158

General Chairs:
Vasudeva Varma,
Subbarao Kambhampati,
Program Chairs:
Arnab Bhattacharya,
Sriraam Natarajan,
Publications Chair:
Rishiraj Saha Roy

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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SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2020

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CoDS COMAD 2020

CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD

January 5 - 7, 2020

Hyderabad, India

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CoDS COMAD 2020 Paper Acceptance Rate 78 of 275 submissions, 28%;

Overall Acceptance Rate 197 of 680 submissions, 29%

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Cited By

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Ghafouri ANaderi HFirouzmandi M(2024)PinLID: a dataset for Pinglish language identiftcation based on code-mixing sentence on unstructured resourcesLanguage Resources and Evaluation10.1007/s10579-024-09783-3Online publication date: 7-Dec-2024
https://doi.org/10.1007/s10579-024-09783-3
Bhalla AChadha A(2023)An Efficient Model to Detect the Presence of Hinglish Text in YouTube Data2023 International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT)10.1109/ICAICCIT60255.2023.10465821(385-391)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICAICCIT60255.2023.10465821

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