research-article

Federated Multi-task Learning for Complaint Identification from Social Media Data

Authors:

Mohammed HasanuzzamanAuthors Info & Claims

HT '21: Proceedings of the 32nd ACM Conference on Hypertext and Social Media

Pages 201 - 210

https://doi.org/10.1145/3465336.3475119

Published: 29 August 2021 Publication History

Abstract

Complaining is a speech act that is often used by consumers to signify a breach of expectation, i.e., an expression of displeasure on a consumer's behalf towards an organization, product, or event. Complaint identification has been previously analyzed based on extensive feature engineering in centralized settings, disregarding the non-identically independently distributed (non-IID), security, and privacy-preserving characteristics of complaints that can hamper data accumulation, distribution, and learning. In this work, we propose a Bidirectional Encoder Representations from Transformers (BERT) based multi-task framework that aims to learn two closely related tasks,viz. complaint identification (primary task) and sentiment classification (auxiliary tasks) concurrently under federated-learning settings. Extensive evaluation on two real-world datasets shows that our proposed framework surpasses the baselines and state-of-the-art framework results by a significant margin.

References

[1]

Mart'i n Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep Learning with Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, October 24--28, 2016, Edgar R. Weippl, Stefan Katzenbeisser, Christopher Kruegel, Andrew C. Myers, and Shai Halevi (Eds.). ACM, 308--318. https://doi.org/10.1145/2976749.2978318

Digital Library

[2]

Shad Akhtar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya, and Sadao Kurohashi. 2019. All-in-one: Emotion, sentiment and intensity prediction using a multi-task ensemble framework. IEEE Transactions on Affective Computing (2019).

[3]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1409.0473

[4]

Xuemei Bai. 2018. Text classification based on LS™ and attention. In 2018 Thirteenth International Conference on Digital Information Management (ICDIM), Berlin, Germany, September 24--26, 2018. IEEE, 29--32. https://doi.org/10.1109/ICDIM.2018.8847061

[5]

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical Secure Aggregation for Privacy-Preserving Machine Learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017, Bhavani M. Thuraisingham, David Evans, Tal Malkin, and Dongyan Xu (Eds.). ACM, 1175--1191. https://doi.org/10.1145/3133956.3133982

Digital Library

[6]

Rich Caruana. 1997. Multitask learning. Machine learning, Vol. 28, 1 (1997), 41--75.

[7]

Rich Caruana and Virginia R. de Sa. 1996. Promoting Poor Features to Supervisors: Some Inputs Work Better as Outputs. In Advances in Neural Information Processing Systems 9, NIPS, Denver, CO, USA, December 2--5, 1996, Michael Mozer, Michael I. Jordan, and Thomas Petsche (Eds.). MIT Press, 389--395. http://papers.nips.cc/paper/1231-promoting-poor-features-to-supervisors-some-inputs-work-better-as-outputs

[8]

KyungHyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. CoRR, Vol. abs/1409.1259 (2014). arxiv: 1409.1259 http://arxiv.org/abs/1409.1259

[9]

Francc ois Chollet et al. 2015. keras.

[10]

Kristof Coussement and Dirk Van den Poel. 2008. Improving customer complaint management by automatic email classification using linguistic style features as predictors. Decision Support Systems, Vol. 44, 4 (2008), 870--882.

Digital Library

[11]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2--7, 2019, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, 4171--4186. https://doi.org/10.18653/v1/n19--1423

[12]

Jiachen Du, Lin Gui, Ruifeng Xu, and Yulan He. 2017. A Convolutional Attention Model for Text Classification. In Natural Language Processing and Chinese Computing - 6th CCF International Conference, NLPCC 2017, Dalian, China, November 8--12, 2017, Proceedings (Lecture Notes in Computer Science, Vol. 10619), Xuanjing Huang, Jing Jiang, Dongyan Zhao, Yansong Feng, and Yu Hong (Eds.). Springer, 183--195. https://doi.org/10.1007/978--3--319--73618--1_16

[13]

Avishek Ghosh, Justin Hong, Dong Yin, and Kannan Ramchandran. 2019. Robust Federated Learning in a Heterogeneous Environment. CoRR, Vol. abs/1906.06629 (2019). arxiv: 1906.06629 http://arxiv.org/abs/1906.06629

[14]

Priya Goyal, Piotr Dollá r, Ross B. Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. 2017. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. CoRR, Vol. abs/1706.02677 (2017). arxiv: 1706.02677 http://arxiv.org/abs/1706.02677

[15]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.

[16]

Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, and Dianbo Liu. 2020. LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data. Plos one, Vol. 15, 4 (2020), e0230706.

[17]

Clayton J. Hutto and Eric Gilbert. 2014. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In Proceedings of the Eighth International Conference on Weblogs and Social Media, ICWSM 2014, Ann Arbor, Michigan, USA, June 1--4, 2014, Eytan Adar, Paul Resnick, Munmun De Choudhury, Bernie Hogan, and Alice H. Oh (Eds.). The AAAI Press. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109

[18]

Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. 2018. Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data. CoRR, Vol. abs/1811.11479 (2018). arxiv: 1811.11479 http://arxiv.org/abs/1811.11479

[19]

Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Doha, Qatar, 1746--1751. https://doi.org/10.3115/v1/D14--1181

[20]

Abhishek Kumar, Asif Ekbal, Daisuke Kawahara, and Sadao Kurohashi. 2019. Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis. In International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, July 14--19, 2019. IEEE, 1--8. https://doi.org/10.1109/IJCNN.2019.8852352

[21]

M Lailiyah, S Sumpeno, and IK E Purnama. 2017. Sentiment analysis of public complaints using lexical resources between Indonesian sentiment lexicon and Sentiwordnet. In 2017 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 307--312.

[22]

Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 2020. On the Variance of the Adaptive Learning Rate and Beyond. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net. https://openreview.net/forum?id=rkgz2aEKDr

[23]

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agü era y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20--22 April 2017, Fort Lauderdale, FL, USA (Proceedings of Machine Learning Research, Vol. 54), Aarti Singh and Xiaojin (Jerry) Zhu (Eds.). PMLR, 1273--1282. http://proceedings.mlr.press/v54/mcmahan17a.html

[24]

H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. 2018. Learning Differentially Private Recurrent Language Models. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=BJ0hF1Z0b

[25]

E Olshtain and L Weinbach. 1985. Complaints: A Study of Speech Act Behavior among Native and Nonnative Speakers of Hebrew. The Prag-matic Perspective.

[26]

Sachin Pawar, Nitin Ramrakhiyani, Girish K. Palshikar, and Swapnil Hingmire. 2015. Deciphering Review Comments: Identifying Suggestions, Appreciations and Complaints. In Natural Language Processing and Information Systems - 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015 Passau, Germany, June 17--19, 2015 Proceedings (Lecture Notes in Computer Science, Vol. 9103), Chris Biemann, Siegfried Handschuh, André Freitas, Farid Meziane, and Elisabeth Mé tais (Eds.). Springer, 204--211. https://doi.org/10.1007/978--3--319--19581-0_18

[27]

Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. Journal of machine learning research, Vol. 12, Oct (2011), 2825--2830.

Digital Library

[28]

Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25--29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, Alessandro Moschitti, Bo Pang, and Walter Daelemans (Eds.). ACL, 1532--1543. https://doi.org/10.3115/v1/d14--1162

[29]

Daniel Preotiuc-Pietro, Mihaela Gaman, and Nikolaos Aletras. 2019. Automatically Identifying Complaints in Social Media. In Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, Anna Korhonen, David R. Traum, and Llu'i s Mà rquez (Eds.). Association for Computational Linguistics, 5008--5019. https://doi.org/10.18653/v1/p19--1495

[30]

Syed Arbaaz Qureshi, Gael Dias, Mohammed Hasanuzzaman, and Sriparna Saha. 2020. Improving depression level estimation by concurrently learning emotion intensity. IEEE Computational Intelligence Magazine, Vol. 15, 3 (2020), 47--59.

[31]

Syed Arbaaz Qureshi, Sriparna Saha, Mohammed Hasanuzzaman, and Gaël Dias. 2019. Multitask Representation Learning for Multimodal Estimation of Depression Level. IEEE Intelligent Systems, Vol. 34, 5 (2019), 45--52.

[32]

Apoorva Singh and Sriparna Saha. 2021. Are You Really Complaining? A Multi-task Framework for Complaint Identification, Emotion and Sentiment Classification. In Proceedings of the 16th International Conference on Document Analysis and Recognition. Springer, Accepted.

Digital Library

[33]

Apoorva Singh, Sriparna Saha, Md Hasanuzzaman, and Kuntal Dey. 2021 a. Multitask learning for complaint identification and sentiment analysis. Cognitive Computation (2021), 1--16.

[34]

Apoorva Singh, Sriparna Saha, Md Hasanuzzaman, and Anubhav Jangra. 2021 b. Identifying Complaints based on Semi-Supervised Mincuts. In Expert System with Applications. Elsevier, Accepted.

[35]

Raghvendra Pratap Singh, Rejwanul Haque, Mohammed Hasanuzzaman, and Andy Way. 2020. Identifying Complaints from Product Reviews: A Case Study on Hindi. In Proceedings of The 28th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Republic of Ireland, December 7--8, 2020 (CEUR Workshop Proceedings, Vol. 2771), Luca Longo, Lucas Rizzo, Elizabeth Hunter, and Arjun Pakrashi (Eds.). CEUR-WS.org, 217--228. http://ceur-ws.org/Vol-2771/AICS2020_paper_28.pdf

[36]

Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet S. Talwalkar. 2017. Federated Multi-Task Learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 4424--4434. https://proceedings.neurips.cc/paper/2017/hash/6211080fa89981f66b1a0c9d55c61d0f-Abstract.html

[37]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, Vol. 15, 1 (2014), 1929--1958.

[38]

Suhatati Tjandra, Amelia Alexandra Putri Warsito, and Judi Prajetno Sugiono. 2015. Determining citizen complaints to the appropriate government departments using KNN algorithm. In 2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015). IEEE, 1--4.

[39]

Camilla Vásquez. 2011. Complaints online: The case of TripAdvisor. Journal of Pragmatics, Vol. 43, 6 (2011), 1707--1717.

[40]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention Is All You Need. CoRR, Vol. abs/1706.03762 (2017). arxiv: 1706.03762 http://arxiv.org/abs/1706.03762

[41]

Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019 a. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 10, 2 (2019), 1--19.

Digital Library

[42]

Wei Yang, Luchen Tan, Chunwei Lu, Anqi Cui, Han Li, Xi Chen, Kun Xiong, Muzi Wang, Ming Li, Jian Pei, and Jimmy Lin. 2019 b. Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2--7, 2019, Volume 2 (Industry Papers), Anastassia Loukina, Michelle Morales, and Rohit Kumar (Eds.). Association for Computational Linguistics, 56--63. https://doi.org/10.18653/v1/n19--2008

[43]

Chunting Zhou, Chonglin Sun, Zhiyuan Liu, and Francis C. M. Lau. 2015. A C-LS™ Neural Network for Text Classification. CoRR, Vol. abs/1511.08630 (2015). arxiv: 1511.08630 http://arxiv.org/abs/1511.08630

Cited By

Singh ABhatia RSaha S(2024)Complaint and Severity Identification From Online Financial ContentIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321552811:1(660-670)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3215528
Singh AChandrasekar SSen TSaha S(2024)Federated Multitask Learning for Complaint Identification Using Graph Attention NetworkIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32851965:3(1277-1286)Online publication date: Mar-2024
https://doi.org/10.1109/TAI.2023.3285196
Ma HGuo HLau V(2023)Communication-Efficient Federated Multitask Learning Over Wireless NetworksIEEE Internet of Things Journal10.1109/JIOT.2022.320131010:1(609-624)Online publication date: 1-Jan-2023
https://doi.org/10.1109/JIOT.2022.3201310
Show More Cited By

Index Terms

Federated Multi-task Learning for Complaint Identification from Social Media Data
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications
    1. Decision support systems
      1. Expert systems

Recommendations

Consumer complaint behaviour in telecommunications

This work analyses the post-purchase behaviour of mobile phone users once they have experienced a service failure. Taking into account the existing literature on Consumer Complaint Behaviour (CCB), a survey for 4249 individuals in Spain is used for ...
FedBone: Towards Large-Scale Federated Multi-Task Learning
Abstract
Federated multi-task learning (FMTL) has emerged as a promising framework for learning multiple tasks simultaneously with client-aware personalized models. While the majority of studies have focused on dealing with the non-independent and ...
Inductive multi-task learning with multiple view data
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

In many real-world applications, it is becoming common to have data extracted from multiple diverse sources, known as "multi-view" data. Multi-view learning (MVL) has been widely studied in many applications, but existing MVL methods learn a single task ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

HT '21: Proceedings of the 32nd ACM Conference on Hypertext and Social Media

August 2021

306 pages

ISBN:9781450385510

DOI:10.1145/3465336

General Chair:
Owen Conlan
Trinity College Dublin, Ireland
,
Program Chair:
Eelco Herder
Radboud Universiteit Nijmegen, Netherlands

Copyright © 2021 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 August 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

HT '21

Sponsor:

HT '21: 32nd ACM Conference on Hypertext and Social Media

August 30 - September 2, 2021

Virtual Event, USA

Acceptance Rates

Overall Acceptance Rate 378 of 1,158 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
386
Total Downloads

Downloads (Last 12 months)53
Downloads (Last 6 weeks)4

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Singh ABhatia RSaha S(2024)Complaint and Severity Identification From Online Financial ContentIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321552811:1(660-670)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3215528
Singh AChandrasekar SSen TSaha S(2024)Federated Multitask Learning for Complaint Identification Using Graph Attention NetworkIEEE Transactions on Artificial Intelligence10.1109/TAI.2023.32851965:3(1277-1286)Online publication date: Mar-2024
https://doi.org/10.1109/TAI.2023.3285196
Ma HGuo HLau V(2023)Communication-Efficient Federated Multitask Learning Over Wireless NetworksIEEE Internet of Things Journal10.1109/JIOT.2022.320131010:1(609-624)Online publication date: 1-Jan-2023
https://doi.org/10.1109/JIOT.2022.3201310
Sahoo PSaha SMondal SChowdhury SGowda S(2023)Vision Transformer-Based Federated Learning for COVID-19 Detection Using Chest X-RayNeural Information Processing10.1007/978-981-99-1648-1_7(77-88)Online publication date: 15-Apr-2023
https://doi.org/10.1007/978-981-99-1648-1_7

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten