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Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification

Authors:

Pawan GoyalAuthors Info & Claims

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

Pages 239 - 243

https://doi.org/10.1145/3371158.3371194

Published: 15 January 2020 Publication History

Abstract

The task of learning a sentiment classification model that adapts well to any target domain, different from the source domain, is a challenging problem. Majority of the existing approaches focus on learning a common representation by leveraging both source and target data during training. In this paper, we introduce a two-stage training procedure that leverages weakly supervised datasets for developing simple lift-and-shift-based predictive models without being exposed to the target domain during the training phase. Experimental results show that transfer with weak supervision from a source domain to various target domains provides performance very close to that obtained via supervised training on the target domain itself.

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Cited By

Devgun K(2022)Weighted Matrix Mapped CNN model for Optimizing the Sentiment Prediction2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)10.1109/ICCCMLA56841.2022.9989039(355-362)Online publication date: 8-Oct-2022
https://doi.org/10.1109/ICCCMLA56841.2022.9989039
Fu QZhuang YZhu YGuo X(2022)Sleeping Lion or Sick Man? Machine Learning Approaches to Deciphering Heterogeneous Images of Chinese in North AmericaAnnals of the American Association of Geographers10.1080/24694452.2022.2042180112:7(2045-2063)Online publication date: 29-Apr-2022
https://doi.org/10.1080/24694452.2022.2042180
Mukherjee RNaik APoddar SDasgupta SGanguly NDiaz FShah CSuel TCastells PJones RSakai T(2021)Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task ApproachProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463080(2303-2307)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463080
Show More Cited By

Index Terms

Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification
1. Computing methodologies
  1. Machine learning

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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

Copyright © 2020 ACM.

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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

Devgun K(2022)Weighted Matrix Mapped CNN model for Optimizing the Sentiment Prediction2022 IEEE 4th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA)10.1109/ICCCMLA56841.2022.9989039(355-362)Online publication date: 8-Oct-2022
https://doi.org/10.1109/ICCCMLA56841.2022.9989039
Fu QZhuang YZhu YGuo X(2022)Sleeping Lion or Sick Man? Machine Learning Approaches to Deciphering Heterogeneous Images of Chinese in North AmericaAnnals of the American Association of Geographers10.1080/24694452.2022.2042180112:7(2045-2063)Online publication date: 29-Apr-2022
https://doi.org/10.1080/24694452.2022.2042180
Mukherjee RNaik APoddar SDasgupta SGanguly NDiaz FShah CSuel TCastells PJones RSakai T(2021)Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task ApproachProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463080(2303-2307)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463080
Geethapriya AValli S(2021)An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment AnalysisInformation Systems Frontiers10.1007/s10796-020-10094-5Online publication date: 5-Jan-2021
https://doi.org/10.1007/s10796-020-10094-5

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