short-paper

SG++: Word Representation with Sentiment and Negation for Twitter Sentiment Classification

Authors:

Liang HeAuthors Info & Claims

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

Pages 997 - 1000

https://doi.org/10.1145/2911451.2914718

Published: 07 July 2016 Publication History

Abstract

Here we propose an advance Skip-gram model to incorporate both word sentiment and negation information. In particular, there is a a softmax layer for the word sentiment polarity upon the Skip-gram model. Then, two paralleled embedding layers are set up in the same embedding space, one for the affirmative context and the other for the negated context, followed by their loss functions. We evaluate our proposed model on the 2013 and 2014 SemEval data sets. The experimental results show that the proposed approach achieves better performance and learns higher dimensional word embedding informatively on the large-scale data.

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Cited By

Xiang HJinjin ZPeng ZXiaodong M(2023)Hierarchical Neural Network with Serial Attention Mechanism for Review Sentiment ClassifificationNeural Processing Letters10.1007/s11063-023-11308-y55:6(8269-8283)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1007/s11063-023-11308-y
Chen SHu QSong YHe YWu HHe L(2019)Self-Attention based Network For Medical Query Expansion2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852269(1-9)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852269
Bai QHu QGe LHe L(2019)Stories That Big Danmaku Data Can Tell as a New MediaIEEE Access10.1109/ACCESS.2019.29090547(53509-53519)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2909054
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Index Terms

SG++: Word Representation with Sentiment and Negation for Twitter Sentiment Classification
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools

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Published In

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

Copyright © 2016 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]

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2016

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The Science and Technology Commission of Shanghai Municipality of China
The National High Technology Research and Development Program of China

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SIGIR '16

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SIGIR

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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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

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

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Cited By

Xiang HJinjin ZPeng ZXiaodong M(2023)Hierarchical Neural Network with Serial Attention Mechanism for Review Sentiment ClassifificationNeural Processing Letters10.1007/s11063-023-11308-y55:6(8269-8283)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1007/s11063-023-11308-y
Chen SHu QSong YHe YWu HHe L(2019)Self-Attention based Network For Medical Query Expansion2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8852269(1-9)Online publication date: Jul-2019
https://doi.org/10.1109/IJCNN.2019.8852269
Bai QHu QGe LHe L(2019)Stories That Big Danmaku Data Can Tell as a New MediaIEEE Access10.1109/ACCESS.2019.29090547(53509-53519)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2909054
Magdy AAbdelhafeez LKang YOng EMokbel M(2019)Microblogs data management: a surveyThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00569-629:1(177-216)Online publication date: 18-Sep-2019
https://dl.acm.org/doi/10.1007/s00778-019-00569-6
Hu QZhou JChen QHe LMcIlraith SWeinberger K(2018)SNNNProceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence10.5555/3504035.3504433(3255-3262)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.5555/3504035.3504433
Chen QHu QHuang JHe L(2018)Modeling Queries with Contextual Snippets for Information RetrievalACM Transactions on Intelligent Systems and Technology10.1145/31616079:4(1-26)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1145/3161607
Yang QLi RLi YLiu Q(2018)Topic-Bigram Enhanced Word Embedding ModelNeural Information Processing10.1007/978-3-030-04182-3_7(69-81)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.1007/978-3-030-04182-3_7
An WChen QYang YHe L(2017)Knowledge Memory Based LSTM Model for Answer SelectionNeural Information Processing10.1007/978-3-319-70096-0_4(34-42)Online publication date: 26-Oct-2017
https://doi.org/10.1007/978-3-319-70096-0_4

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