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Aspect Level Sentiment Classification Based on Double Attention Mechanism

Published: 19 March 2020 Publication History

Abstract

Aspect sentiment classification is a fine-grained sentiment classification method, which is used to identify the sentimental polarity of a given aspect word in one sentence. Among the existing aspect-level sentiment classification methods, the deep learning model with the attention mechanism solves the problem of key word recognition in sentiment analysis and achieves good results. However, the effect of sentiment classification is not good in complex sentence structure and informal expression. In the deep learning model of aspect-level sentiment classification, this paper combines internal attention with external attention, and constructs an aspect-level sentiment classification model based on double attention, which consider the internal structure of the text as well as the external attention concerns of people. Experiments were conducted on SemEval2014 and Twitter datasets. Compared with the classical classification methods, the recognition accuracy of the proposed algorithm in this paper was improved by about 1%, and better results were achieved.

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Cited By

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  • (2022)Sentiment Analysis of Weibo Short Text Based on Attention Mechanism and BERT Model2022 4th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP55136.2022.00095(520-524)Online publication date: Mar-2022
  • (2021)Aspect-Based Sentiment Analysis of Individual Equipment Ergonomics Evaluation2021 International Conference on Computer, Control and Robotics (ICCCR)10.1109/ICCCR49711.2021.9349382(173-180)Online publication date: 8-Jan-2021
  • (2021)A Deep Learning Approach for Aspect Sentiment Triplet Extraction in PortugueseIntelligent Systems10.1007/978-3-030-91699-2_24(343-358)Online publication date: 28-Nov-2021

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cover image ACM Other conferences
EBIMCS '19: Proceedings of the 2019 2nd International Conference on E-Business, Information Management and Computer Science
August 2019
175 pages
ISBN:9781450366496
DOI:10.1145/3377817
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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Association for Computing Machinery

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Publication History

Published: 19 March 2020

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Author Tags

  1. Aspect level
  2. Double Attention
  3. Sentiment classification

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  • Research-article
  • Research
  • Refereed limited

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  • Guangxi Young and middle-aged teachers' basic ability improvement project

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EBIMCS '19

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EBIMCS '19 Paper Acceptance Rate 31 of 142 submissions, 22%;
Overall Acceptance Rate 143 of 708 submissions, 20%

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Cited By

View all
  • (2022)Sentiment Analysis of Weibo Short Text Based on Attention Mechanism and BERT Model2022 4th International Conference on Natural Language Processing (ICNLP)10.1109/ICNLP55136.2022.00095(520-524)Online publication date: Mar-2022
  • (2021)Aspect-Based Sentiment Analysis of Individual Equipment Ergonomics Evaluation2021 International Conference on Computer, Control and Robotics (ICCCR)10.1109/ICCCR49711.2021.9349382(173-180)Online publication date: 8-Jan-2021
  • (2021)A Deep Learning Approach for Aspect Sentiment Triplet Extraction in PortugueseIntelligent Systems10.1007/978-3-030-91699-2_24(343-358)Online publication date: 28-Nov-2021

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