skip to main content
10.1145/3209978.3210115acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
short-paper

Convolution-based Memory Network for Aspect-based Sentiment Analysis

Published: 27 June 2018 Publication History

Abstract

Memory networks have shown expressive performance on aspect based sentiment analysis. However, ordinary memory networks only capture word-level information and lack the capacity for modeling complicated expressions which consist of multiple words. Targeting this problem, we propose a novel convolutional memory network which incorporates an attention mechanism. This model sequentially computes the weights of multiple memory units corresponding to multi-words. This model may capture both words and multi-words expressions in sentences for aspect-based sentiment analysis. Experimental results show that the proposed model outperforms the state-of-the-art baselines.

References

[1]
Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In KDD, pages 168--177, 2004.
[2]
Yoon Kim. Convolutional neural networks for sentiment classification. In EMNLP, pages 1746--1751, 2014.
[3]
Svetlana Kiritchenko, Xiaodan Zhu, Colin. Cherry, and Saif Mohammad. Nrc-canada-2014: Detecting aspects and sentiment in customer reviews. In SemEval, pages 437--442, 2014.
[4]
Dong Li, Furu Wei, Duyu Tang Chuanqi Tan, Ming Zhou, and Ke Xu. Adaptive recursive neural network for target-dependent twitter sentiment classification. In ACL, pages 49--54, 2014.
[5]
Duyu Tang, Bing Qin, Xiaocheng Feng, and Ting Liu. Target-dependent sentiment classification with long short term memory. CoRR, abs/1512.01100, 2015.
[6]
Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al. End-to-end memory networks. In NIPS, pages 2440--2448, 2015.
[7]
Lin Gui, Jiannan Hu, Yulan He, Ruifeng Xu, Qin Lu, and Jiachen Du. A question answering approach to emotion cause extraction. In EMNLP, pages 1593--1602, 2017.
[8]
Duyu Tang, Bing Qin, and Ting Liu. Aspect level sentiment classification with deep memory network. In EMNLP, pages 214--224, 2016.
[9]
Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. Recurrent attention network on memory for aspect sentiment analysis. In EMNLP, pages 452--461, 2017.
[10]
Cheng Li and Qiaozhu Mei. Deep memory networks for attitude identification. In WSDM, pages 671--680, 2017.
[11]
Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global vectors for word representation. In EMNLP, pages 1532--1543, 2014.
[12]
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. Semeval-2014 task 4: Aspect based sentiment analysis. In SemEval, pages 27--35, 2014.

Cited By

View all
  • (2024)A Hybrid Approach to Dimensional Aspect-Based Sentiment Analysis Using BERT and Large Language ModelsElectronics10.3390/electronics1318372413:18(3724)Online publication date: 19-Sep-2024
  • (2024)An aspect sentiment analysis model with Aspect Gated Convolution and Dual-Feature Filtering layersJournal of Big Data10.1186/s40537-024-00969-811:1Online publication date: 9-Aug-2024
  • (2024)Learning Polarity Embedding Attention for Aspect-based Sentiment AnalysisInternational Journal on Artificial Intelligence Tools10.1142/S021821302350054933:01Online publication date: 23-Feb-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
June 2018
1509 pages
ISBN:9781450356572
DOI:10.1145/3209978
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: 27 June 2018

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Short-paper

Funding Sources

  • Key Technologies Research and Development Program of Shenzhen
  • Shenzhen Foundational Research Funding
  • Innovate UK
  • National Natural science Foundation of China
  • EU-H2020
  • National Key research and Development Program of China

Conference

SIGIR '18
Sponsor:

Acceptance Rates

SIGIR '18 Paper Acceptance Rate 86 of 409 submissions, 21%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)7
  • Downloads (Last 6 weeks)0
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)A Hybrid Approach to Dimensional Aspect-Based Sentiment Analysis Using BERT and Large Language ModelsElectronics10.3390/electronics1318372413:18(3724)Online publication date: 19-Sep-2024
  • (2024)An aspect sentiment analysis model with Aspect Gated Convolution and Dual-Feature Filtering layersJournal of Big Data10.1186/s40537-024-00969-811:1Online publication date: 9-Aug-2024
  • (2024)Learning Polarity Embedding Attention for Aspect-based Sentiment AnalysisInternational Journal on Artificial Intelligence Tools10.1142/S021821302350054933:01Online publication date: 23-Feb-2024
  • (2024)Graph Augmentation Networks Based on Dynamic Sentiment Knowledge and Static External Knowledge Graphs for aspect-based sentiment analysisExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123981251:COnline publication date: 24-Jul-2024
  • (2024)Polarity-aware deep attention network for aspect-based sentiment analysisProgress in Artificial Intelligence10.1007/s13748-024-00352-xOnline publication date: 4-Nov-2024
  • (2023)PHNN: A Prompt and Hybrid Neural Network-Based Model for Aspect-Based Sentiment ClassificationElectronics10.3390/electronics1219412612:19(4126)Online publication date: 3-Oct-2023
  • (2023)Dual-Track Lifelong Machine Learning-Based Fine-Grained Product Quality AnalysisApplied Sciences10.3390/app1303124113:3(1241)Online publication date: 17-Jan-2023
  • (2023)Aspect-level multimodal co-attention graph convolutional sentiment analysis modelJournal of Image and Graphics10.11834/jig.22101528:12(3838-3854)Online publication date: 2023
  • (2023)Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-Based Sentiment AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.325049935:10(10098-10111)Online publication date: 1-Oct-2023
  • (2023)An Interactive Attention Mechanism Fusion Network for Aspect-Based Multimodal Sentiment Analysis2023 International Conference on Machine Learning and Cybernetics (ICMLC)10.1109/ICMLC58545.2023.10327929(268-275)Online publication date: 9-Jul-2023
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media