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Fusion Local and Global Aspect-based Sentiment Analysis

Published: 20 August 2023 Publication History

Abstract

Aspect-based sentiment classification is an important task in natural language processing research, and in response to the fact that most studies at this stage ignore the influence of contextual semantic information on the sentiment polarity of aspect words, our model proposed in this paper combines local aspect word feature extraction and global contextual semantic information extraction based on Bi-directional Long Short-Term Memory (BiLSTM), and after a multi-headed attention mechanism to enhance the local aspect word sentiment representation. Comparative experiments were conducted on the restaurant and laptop datasets of the SEMEVAL2014 evaluation task. The experimental results show that the model proposed in this paper achieves good classification results in the aspect-level sentiment analysis task of text reviews. The method provides a new idea for ABSA task development.

References

[1]
Pontiki, M.; Galanis, D.; Pavlopoulos, J.; Papageorgiou, H.;Androutsopoulos, I.; and Manandhar, S. 2014. Semeval 2014 task 4:
[2]
Aspect based sentiment analysis. In SemEval@COLING 2014, 27–35.
[3]
Shinhyeok Oh1, Dongyub Lee2*, Taesun Whang Deep Context-and Relation-Aware Learningfor Aspect-based Sentiment Analysis,arXiv:2016.038.6v1.2021.
[4]
Zeng B, Yang H, Xu R, Lcf: A local context focus mechanism for aspect-based sentiment classification[J]. Applied Sciences, 2019, 9(16): 3389.
[5]
Wu Z, Ong D C. Context-guided bert for targeted aspect-based sentiment analysis[C]//Proceedings of the AAAI Conference on Artificial Intelligence, Online, Feb 2-9, 2021, 35(16): 14094- 14102. Hochreiter S, Schmidhuber J.Long short-term memory[J].Neural Computation, 1997, 9(8):1735-1780.
[6]
WU B R, QIAO H, JIA Y F, Sentiment Analysis of Mid length Microblog Based on Capsule Network[J]. Journal of Signal Processing,2022,38(8):1632-1641. issn.1003-0530.2022.08.008.
[7]
TANG D Y, QIN B, FENG X C, Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers, 2016: 3298-3307
[8]
Hong Chen, Yan Yang, Shengdong Du. User comment aspect level sentiment analysis research[J]. Computer science and Exploration, 2021, 15(3): 478-485.
[9]
Zhidong Xu, Binyang Chen, Xiao Wang. Research on aspect level emotion classification based on capsule network[J]. Journal of Intelligent Science and Technology, 2020, 2(3): 284-292
[10]
TIN SONG, Zhanwei Chen, Haifeng Yang. Aspect emotion analysis based on hierarchical attention network[J]. Big data, 2020, 6(5): 82-91.
[11]
Peng, H., Ma, Y., Li, Y., Cambria, E., 2018. Learning multi-grained aspect target sequence for chinese sentiment analysis. Knowledge-Based Systems 148, 167–176.
[12]
Chen, F., Huang, Y., 2019. Knowledge-enhanced neural networks for sentiment analysis of chinese reviews. Neurocomputing 368, 51 – 58.
[13]
Liu, N., Shen, B., 2019. Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems, 105010.
[14]
Zeng, Z., Ma, J., Chen, M., Li, X., 2019b. Joint learning for aspect category detection and sentiment analysis in chinese reviews, in: China Conference on Information Retrieval, Springer. pp. 108–120.
[15]
WANG Y Q, HUANG M L, ZHU X Y, Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 606-615.
[16]
Ma, D., Li, S., Zhang, X. & Wang, H. Interactive attention networks for aspect-level sentiment classification[C]. Proceedings of the twenty-sixth international joint conference on artificial intelligence.2017:4069–4074.
[17]
Chi Sun, Luyao Huang, Xipeng Qiu*. Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence.[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics :Human Language Technologies,2019:380-385
[18]
LI Pan,WU Yadong,CHU QikaiFU,Chaoshuai,ZHANG Guiyu,et al.Aspect-based sentiment analysis of long text based on BERT and memory network [J]. Transducer and Microsystem Technologies,2022(2):118-122.

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ICCTA '23: Proceedings of the 2023 9th International Conference on Computer Technology Applications
May 2023
270 pages
ISBN:9781450399579
DOI:10.1145/3605423
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 the author(s) 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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Published: 20 August 2023

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Author Tags

  1. BERT
  2. BiLSTM
  3. aspect-level
  4. interactive attention
  5. sentiment analysis

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