research-article

Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural Networks

Authors:

Jimmy Xiangji Huang,

Tingting HeAuthors Info & Claims

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 245 - 254

https://doi.org/10.1145/2911451.2911490

Published: 07 July 2016 Publication History

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Abstract

NOTE FROM ACM: It has been determined that this article plagiarized the contents of a previously published paper. Therefore ACM has shut off access to this paper.

This article has been removed from the ACM Digital Library because it was found to plagiarize an earlier work written by Xiangbo Shu, Guo-Jin Qi, Jinhui Tang, and Jingdong Wang published by ACM and entitled DOI:http://doi.acm.org/10.1145/2733373.2806216 Weakly-Shared Deep Transder Networks for Heterogeneous-Domain Knowledge Propagation. In Proceedings of the 23rd ACM International Conference on Multimedia (MM '15). ACM, New York, NY, USA, 35-44.

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Cited By

Kumar AMangotra AAilawadi AJain RArora M(2024)Sentiment Analysis on Multilingual Data: HinglishProceedings of Data Analytics and Management10.1007/978-981-99-6547-2_47(607-620)Online publication date: 3-Jan-2024
https://doi.org/10.1007/978-981-99-6547-2_47
Rajesh AHiwarkar T(2024)Sentiment Analysis Using Ensemble of Deep Learning ModelsProceedings of International Conference on Computational Intelligence10.1007/978-981-97-3526-6_37(471-483)Online publication date: 18-Jul-2024
https://doi.org/10.1007/978-981-97-3526-6_37
Baddur RShedole S(2021)A Novel Approach for Sentiment Analysis Using Deep Recurrent Networks and Sequence ModelingRecent Patents on Engineering10.2174/187221211366619031516300914:3(403-411)Online publication date: 19-Jan-2021
https://doi.org/10.2174/1872212113666190315163009
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Index Terms

Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural Networks
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

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Published: 07 July 2016

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SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Kumar AMangotra AAilawadi AJain RArora M(2024)Sentiment Analysis on Multilingual Data: HinglishProceedings of Data Analytics and Management10.1007/978-981-99-6547-2_47(607-620)Online publication date: 3-Jan-2024
https://doi.org/10.1007/978-981-99-6547-2_47
Rajesh AHiwarkar T(2024)Sentiment Analysis Using Ensemble of Deep Learning ModelsProceedings of International Conference on Computational Intelligence10.1007/978-981-97-3526-6_37(471-483)Online publication date: 18-Jul-2024
https://doi.org/10.1007/978-981-97-3526-6_37
Baddur RShedole S(2021)A Novel Approach for Sentiment Analysis Using Deep Recurrent Networks and Sequence ModelingRecent Patents on Engineering10.2174/187221211366619031516300914:3(403-411)Online publication date: 19-Jan-2021
https://doi.org/10.2174/1872212113666190315163009
Wang WPan S(2020)Syntactically Meaningful and Transferable Recursive Neural Networks for Aspect and Opinion ExtractionComputational Linguistics10.1162/coli_a_0036245:4(705-736)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1162/coli_a_00362
Zhang YZong RWang DAlhajj R(2020)A hybrid transfer learning approach to migratable disaster assessment in social media sensingProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381433(131-138)Online publication date: 7-Dec-2020
https://dl.acm.org/doi/10.1109/ASONAM49781.2020.9381433
Daou H(2020)Sentiment of the public: the role of social media in revealing important eventsOnline Information Review10.1108/OIR-12-2019-037345:1(157-173)Online publication date: 27-Nov-2020
https://doi.org/10.1108/OIR-12-2019-0373
Chandra GDwivedi S(2020)Query expansion based on term selection for Hindi – English cross lingual IRJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2017.09.00232:3(310-319)Online publication date: 1-Mar-2020
https://dl.acm.org/doi/10.1016/j.jksuci.2017.09.002
Wei PKe YGoh C(2019)Feature Analysis of Marginalized Stacked Denoising Autoenconder for Unsupervised Domain AdaptationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2018.286870930:5(1321-1334)Online publication date: May-2019
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Arora MKansal V(2019)Character level embedding with deep convolutional neural network for text normalization of unstructured data for Twitter sentiment analysisSocial Network Analysis and Mining10.1007/s13278-019-0557-y9:1Online publication date: 18-Mar-2019
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Fernández AEsuli ASebastiani F(2018)Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification (extended abstract)Proceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304817(5647-5651)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304652.3304817
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