research-article

CrowdTC: Crowd-powered Learning for Text Classification

Authors:

Baihua ZhengAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 1

Article No.: 15, Pages 1 - 23

https://doi.org/10.1145/3457216

Published: 20 July 2021 Publication History

Abstract

Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform to extract keywords in text. Sampling and clustering techniques are utilized to reduce the cost of crowdsourcing. Also, we present an attention-based neural network and a hybrid neural network to incorporate the extracted keywords as human guidance into deep neural networks. Extensive experiments on public datasets confirm that CrowdTC improves the text classification accuracy of neural networks by using the crowd-powered keyword guidance.

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Cited By

Maruf AKhanam FHaque MJiyad ZMridha MAung Z(2024)Challenges and Opportunities of Text-Based Emotion Detection: A SurveyIEEE Access10.1109/ACCESS.2024.335635712(18416-18450)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3356357
Wu GZhuo XBao XHu XHong RWu X(2023)Crowdsourcing Truth Inference via Reliability-Driven Multi-View Graph EmbeddingACM Transactions on Knowledge Discovery from Data10.1145/356557617:5(1-26)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3565576
Zeng JZhou JLiu T(2023)Robust Multimodal Sentiment Analysis via Tag Encoding of Uncertain Missing ModalitiesIEEE Transactions on Multimedia10.1109/TMM.2022.320757225(6301-6314)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3207572
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Index Terms

CrowdTC: Crowd-powered Learning for Text Classification
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection
      2. Document topic models

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 1

February 2022

475 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3472794

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 20 July 2021

Accepted: 01 March 2021

Revised: 01 March 2021

Received: 01 November 2020

Published in TKDD Volume 16, Issue 1

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Cited By

Maruf AKhanam FHaque MJiyad ZMridha MAung Z(2024)Challenges and Opportunities of Text-Based Emotion Detection: A SurveyIEEE Access10.1109/ACCESS.2024.335635712(18416-18450)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3356357
Wu GZhuo XBao XHu XHong RWu X(2023)Crowdsourcing Truth Inference via Reliability-Driven Multi-View Graph EmbeddingACM Transactions on Knowledge Discovery from Data10.1145/356557617:5(1-26)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3565576
Zeng JZhou JLiu T(2023)Robust Multimodal Sentiment Analysis via Tag Encoding of Uncertain Missing ModalitiesIEEE Transactions on Multimedia10.1109/TMM.2022.320757225(6301-6314)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2022.3207572
Yu CLiu HWahab FLing ZRen TMa HZhao Y(2023)Global triangle estimation based on first edge sampling in large graph streamsThe Journal of Supercomputing10.1007/s11227-023-05205-379:13(14079-14116)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1007/s11227-023-05205-3
Wu GZhuo XZhou LBao XHong RWu X(2022)TIRA: Truth Inference via Reliability Aggregation on Object-Source GraphIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322530835:11(11967-11981)Online publication date: 29-Nov-2022
https://dl.acm.org/doi/10.1109/TKDE.2022.3225308
Wang JLiu B(2022)Recruitment Fraud Detection Method Based on Crowdsourcing and Multi-feature Fusion2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD)10.1109/ICAIBD55127.2022.9820055(267-273)Online publication date: 27-May-2022
https://doi.org/10.1109/ICAIBD55127.2022.9820055

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