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Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification

Authors:

Aurelien Lucchi,

Valeria De Luca,

Aliaksei Severyn,

Mark Cieliebak,

Thomas Hofmann,

Martin JaggiAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 1045 - 1052

https://doi.org/10.1145/3038912.3052611

Published: 03 April 2017 Publication History

Abstract

This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse - but still acceptable - performance when compared to the single language model, while benefiting from better generalization properties across languages.

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https://doi.org/10.3390/s24020348
Ali HHashmi EYayilgan Yildirim SShaikh S(2024)Analyzing Amazon Products Sentiment: A Comparative Study of Machine and Deep Learning, and Transformer-Based TechniquesElectronics10.3390/electronics1307130513:7(1305)Online publication date: 31-Mar-2024
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Muhammad Shahid Iqbal Malik Rehan MNawaz A(2024)Analyzing Cross-Lingual Approaches: a Case Study for Detecting Multilingual Hope Expressions in YouTube CommentsPattern Recognition and Image Analysis10.1134/S105466182470072X34:3(831-843)Online publication date: 17-Oct-2024
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Index Terms

Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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SpinningBytes
Swiss Commission for Technology and Innovation (CTI)

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WWW '17

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IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Khoo LLim MChong CMcNaney R(2024)Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing ApproachesSensors10.3390/s2402034824:2(348)Online publication date: 6-Jan-2024
https://doi.org/10.3390/s24020348
Ali HHashmi EYayilgan Yildirim SShaikh S(2024)Analyzing Amazon Products Sentiment: A Comparative Study of Machine and Deep Learning, and Transformer-Based TechniquesElectronics10.3390/electronics1307130513:7(1305)Online publication date: 31-Mar-2024
https://doi.org/10.3390/electronics13071305
Muhammad Shahid Iqbal Malik Rehan MNawaz A(2024)Analyzing Cross-Lingual Approaches: a Case Study for Detecting Multilingual Hope Expressions in YouTube CommentsPattern Recognition and Image Analysis10.1134/S105466182470072X34:3(831-843)Online publication date: 17-Oct-2024
https://dl.acm.org/doi/10.1134/S105466182470072X
Eyu JYau KLiu LChong Y(2024)Reinforcement learning in sentiment analysis: a review and future directionsArtificial Intelligence Review10.1007/s10462-024-10967-058:1Online publication date: 7-Nov-2024
https://doi.org/10.1007/s10462-024-10967-0
Pólya TCsertő I(2023)Emotion Recognition Based on the Structure of NarrativesElectronics10.3390/electronics1204091912:4(919)Online publication date: 11-Feb-2023
https://doi.org/10.3390/electronics12040919
Zhao SHong XYang JZhao YDing G(2023)Toward Label-Efficient Emotion and Sentiment AnalysisProceedings of the IEEE10.1109/JPROC.2023.3309299111:10(1159-1197)Online publication date: Oct-2023
https://doi.org/10.1109/JPROC.2023.3309299
Raghunathan NSaravanakumar K(2023)Challenges and Issues in Sentiment Analysis: A Comprehensive SurveyIEEE Access10.1109/ACCESS.2023.329304111(69626-69642)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3293041
Mabokela KCelik TRaborife M(2023)Multilingual Sentiment Analysis for Under-Resourced Languages: A Systematic Review of the LandscapeIEEE Access10.1109/ACCESS.2022.322413611(15996-16020)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224136
Mercha EBenbrahim H(2023)Machine learning and deep learning for sentiment analysis across languages: A surveyNeurocomputing10.1016/j.neucom.2023.02.015531(195-216)Online publication date: Apr-2023
https://doi.org/10.1016/j.neucom.2023.02.015
MohammadiBaghmolaei RAhmadi A(2023)TETKnowledge-Based Systems10.1016/j.knosys.2022.110236262:COnline publication date: 28-Feb-2023
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