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Parallel Multi-feature Attention on Neural Sentiment Classification

Published: 07 December 2017 Publication History

Abstract

The analysis of the review's sentiment polarity is a fundamental task in NLP. However, most of the existing sentiment classification models only focus on extracting features but ignore features' own differences. Additionally, these models only pay attention to content information but ignore the user's ranking preference. To address these issues, we propose a novel Parallel Multi-feature Attention (PMA) neural network which concentrates on fine-grained information between user and product level content features. Moreover, we use multi-feature, user's ranking preference included, to improve the performance of sentiment classification. Experimental results on IMDB and Yelp datasets show that PMA model achieves state-of-the-art performance.

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Cited By

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  • (2024)A survey on personalized document-level sentiment analysisNeurocomputing10.1016/j.neucom.2024.128449(128449)Online publication date: Aug-2024
  • (2023)UCM: Personalized Document-Level Sentiment Analysis Based on User Correlation MiningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-99-4752-2_38(456-471)Online publication date: 31-Jul-2023
  • (2019)Categorical Metadata Representation for Customized Text ClassificationTransactions of the Association for Computational Linguistics10.1162/tacl_a_002637(201-215)Online publication date: Nov-2019

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cover image ACM Other conferences
SoICT '17: Proceedings of the 8th International Symposium on Information and Communication Technology
December 2017
486 pages
ISBN:9781450353281
DOI:10.1145/3155133
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]

In-Cooperation

  • SOICT: School of Information and Communication Technology - HUST
  • NAFOSTED: The National Foundation for Science and Technology Development

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 December 2017

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Author Tags

  1. Deep learning
  2. Parallel Multi-feature Attention
  3. Sentiment classification

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SoICT 2017

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Overall Acceptance Rate 147 of 318 submissions, 46%

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Cited By

View all
  • (2024)A survey on personalized document-level sentiment analysisNeurocomputing10.1016/j.neucom.2024.128449(128449)Online publication date: Aug-2024
  • (2023)UCM: Personalized Document-Level Sentiment Analysis Based on User Correlation MiningAdvanced Intelligent Computing Technology and Applications10.1007/978-981-99-4752-2_38(456-471)Online publication date: 31-Jul-2023
  • (2019)Categorical Metadata Representation for Customized Text ClassificationTransactions of the Association for Computational Linguistics10.1162/tacl_a_002637(201-215)Online publication date: Nov-2019

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