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Fine-grained attention-based phrase-aware network for aspect-level sentiment analysis

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Abstract

Aspect-level sentiment classification aims to identify the sentiment polarity of a specific aspect in a sentence. In recent years, many researchers have sought to explore aspect-specific representation via attention mechanisms. Although a remarkable improvement in aspect-level sentiment classification has been achieved, these methods still suffer from deteriorative performance in certain cases for multiple reasons. First, they adopt a coarse-grained attention mechanism, which may cause information loss when the contextual sentence is long or includes multiple sentiments, and the aspect contains multiple words. Second, they consider only the influence of contextual keywords on sentiment polarity, ignoring the importance of key phrases in a sentence. To address these issues, a phrase-aware neural network based on fine-grained attention,referred to as FAPN, is proposed. The FAPN employs a convolutional neural network to extract phrase representations in the context and concatenates the representations with the corresponding word vector as input. Additionally, a fine-grained attention module is designed to generate aspect-specific representations by capturing the word-level interactions between the aspect and the sentence. Extensive experiments on five widely used benchmark datasets demonstrate the effectiveness of the proposed FAPN method.

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References

  • Akhtar MS, Ekbal A, Cambria E (2020) How intense are you? predicting intensities of emotions and sentiments using stacked ensemble [application notes]. IEEE Comput Intell Mag 15(1):64–75

    Article  Google Scholar 

  • Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. CoRR, arXiv:1409.0473

  • Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR (2021) Abcdm: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generat Comput Syst 115:279–294

    Article  Google Scholar 

  • Cambria Erik, Li Yang, Xing Frank Z, Poria Soujanya, Kwok Kenneth (2020) Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 105–114

  • Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107

    Article  Google Scholar 

  • Cavallari S, Zheng V. W, Cai H, Chang K. C, Cambria E (2017) Learning community embedding with community detection and node embedding on graphs. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 377–386

  • Chen P, Sun Z, Bing L, Yang W (2017) Recurrent attention network on memory for aspect sentiment analysis. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 452–461

  • Devlin Jacob, Chang Ming-Wei, Lee Kenton, Toutanova Kristina (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  • Dong L, Wei F, Tan C, Tang D, Zhou M, Xu K (2014) Adaptive recursive neural network for target-dependent twitter sentiment classification. In:Proceedings of the 52nd annual meeting of the association for computational linguistics (volume 2: Short papers), 2:49–54

  • Fan F, Feng Y, Zhao D (2018) Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 conference on empirical methods in natural language processing, pp 3433–3442

  • Fu X, Wei Y, Xu F, Wang T, Lu Y, Li J, Huang J (2019) Semi-supervised aspect-level sentiment classification model based on variational autoencoder. Knowl Based Syst 171:81–92

    Article  Google Scholar 

  • Gan C, Wang L, Zhang Z (2020) Multi-entity sentiment analysis using self-attention based hierarchical dilated convolutional neural network. Future Generat Comput Syst 112:116–125

    Article  Google Scholar 

  • Gan Chenquan, Feng Qingdong, Zhang Zufan (2021) Scalable multi-channel dilated CNN-BiLSTM model with attention mechanism for Chinese textual sentiment analysis. Future Generat Comput Syst 118:297–309

    Article  Google Scholar 

  • Gu S, Zhang L, Hou Y, Song Y (2018) A position-aware bidirectional attention network for aspect-level sentiment analysis. pp 774–784

  • Hermann K. M, Kocisky T, Grefenstette E, Espeholt L, Kay W, Suleyman M, Blunsom P (2015) Teaching machines to read and comprehend. In: Advances in neural information processing systems, pp. 1693–1701

  • Huang B, Carley K Syntax-aware aspect level sentiment classification with graph attention networks. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing

  • Huang B, Ou Y, Carley K. M (2018) Aspect level sentiment classification with attention-over-attention neural networks. In: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation, pp 197–206

  • Hussain A, Cambria E (2018) Semi-supervised learning for big social data analysis. Neurocomputing 275:1662–1673

    Article  Google Scholar 

  • Jiang L, Yu M, Zhou M, Liu X, Zhao T (2011) Target-dependent twitter sentiment classification. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies-volume 1, pp 151–160

  • Johnson R, Zhang T (2015) Semi-supervised convolutional neural networks for text categorization via region embedding. In: Advances in neural information processing systems, pp 919–927

  • Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1746–1751

  • Kiritchenko S, Zhu X, Cherry C, Mohammad S (2014) Nrc-canada-2014: Detecting aspects and sentiment in customer reviews. In: Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp. 437–442

  • Li Wei, Zhu Luyao, Shi Yong, Guo Kun, Cambria Erik (2020) User reviews: sentiment analysis using lexicon integrated two-channel CNN-LSTM family models. Appl Soft Comput 94:106435

    Article  Google Scholar 

  • Li X, Bing L, Lam W, Shi B (2018) Transformation networks for target-oriented sentiment classification. ACL, pages 946–956

  • Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 5(1):1–167

    Article  Google Scholar 

  • Liu J, Zhang Y (2017) Attention modeling for targeted sentiment. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pp 572–577

  • Li Z, Wei Y, Zhang Y, Zhang X, Li X (2019) Exploiting coarse-to-fine task transfer for aspect-level sentiment classification. 33:4253–4260

  • Li H, Xue Y, Zhao H, Hu X, Peng S (2019) Co-attention networks for aspect-level sentiment analysis. pp 200–209

  • Luong M, Pham H, Manning C. (2015). Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025

  • Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. IJCAI, pp 4068–4074

  • Mihalcea R, Garimella A (2016) What men say, what women hear: finding gender-specific meaning shades. IEEE Intell Syst 31(4):62–67

    Article  Google Scholar 

  • Mnih V, Heess N, Graves A (2014) Recurrent models of visual attention. In: Advances in neural information processing systems, pp 2204–2212

  • Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807–814

  • Nguyen TH, Shirai K (2015) Phrasernn: phrase recursive neural network for aspect-based sentiment analysis. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 2509–2514

  • Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends® Inf Retrieval, 2(1–2):1–135

  • Pennington J, Socher R, Manning C (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  • Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, Alsmadi M, Alayyoub M, Zhao Y, Qin B (2016) De C. Semeval-2016 task 5 : aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 19–30

  • Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I (2016) Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016)

  • Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) Semeval-2014 task 4: aspect based sentiment analysis. In: Proceedings of international workshop on semantic evaluation at, pp 27–35

  • Qian Q, Tian B, Huang M, Liu Y, Zhu X, Zhu X (2015) Learning tag embeddings and tag-specific composition functions in recursive neural network. In:Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), 1:1365–1374

  • Qin L, Li Z, Che W, Ni M, Liu T (2020) Co-gat: A co-interactive graph attention network for joint dialog act recognition and sentiment classification. arXiv preprint arXiv:2012.13260

  • Rietzler A, Stabinger S, Opitz P, Engl S (2019) Adapt or get left behind: Domain adaptation through bert language model finetuning for aspect-target sentiment classification. arXiv preprint arXiv:1908.11860

  • Schmitt M, Steinheber S, Schreiber K, Roth B (2018) Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks. pp 1109–1114

  • Socher R, Pennington J, Huang EH, Ng AY, Manning CD (2011) Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Conference on empirical methods in natural language processing, pp 151–161

  • Song Y, Wang J, Jiang T, Liu Z, Rao Y (2019) Attentional encoder network for targeted sentiment classification. arXiv preprint arXiv:1902.09314

  • Tang H, Ji D, Li C, Zhou Q Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings of the 58th annual meeting of the association for computational linguistics

  • Tang J, Lu Z, Su J, Ge Y, Song L, Sun L, Luo J (2019) Progressive self-supervised attention learning for aspect-level sentiment analysis. Computation and Language. arXiv:1906.01213

  • Tang D, Qin B, Feng X, Liu T (2016) Effective lstms for target-dependent sentiment classification. In: International conference on computational linguistics, pp 3298–3307

  • Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network. In: Proceedings of EMNLP, pp 214–224

  • Usama M, Ahmad B, Song E, Hossain MS, Alrashoud M, Muhammad G (2020) Attention-based sentiment analysis using convolutional and recurrent neural network. Future Generat Comput Syst 113:571–578

    Article  Google Scholar 

  • Wagner J, Arora P, Cortes S, Barman U, Bogdanova D, Foster J, Tounsi L (2014) Dcu: Aspect-based polarity classification for semeval task 4. In:Proceedings of the 8th international workshop on semantic evaluation (SemEval 2014), pp 223–229

  • Wang Y, Huang M, Zhao L (2016) Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606–615

  • Xu H, Liu B, Shu L, Yu P. S (2019) Bert post-training for review reading comprehension and aspect-based sentiment analysis. Computation and Language. arXiv:1904.02232

  • Young T, Cambria E, Chaturvedi I, Zhou H, Biswas S, Huang M (2018) Augmenting end-to-end dialogue systems with commonsense knowledge. In: Thirty-Second AAAI conference on artificial intelligence, pp. 4970–4977

  • Yue L, Zhai C (2008) Opinion integration through semi-supervised topic modeling. In: Proceedings of the 17th International World Wide Web Conference, pp 121–130

  • Yukun M, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive lstm. pp 5876–5883

  • Zhang C, Li Q, Song D Aspect-based sentiment classification with aspect-specific graph convolutional networks. In: The 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP2019)

  • Zheng S, Xia R (2018) Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention. arXiv preprint arXiv:1802.00892

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Acknowledgements

This work is supported by the National Key R&D Program of China (2019YFB1704100).

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Weizhi Liao, Jiarui Zhou, Yu Wang & Xiaobing Zhang

  2. Kunming University of Science and Technology, Kunming, China

    Yanchao Yin

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  1. Weizhi Liao
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  2. Jiarui Zhou
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  3. Yu Wang
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  4. Yanchao Yin
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  5. Xiaobing Zhang
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Correspondence to Weizhi Liao.

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Liao, W., Zhou, J., Wang, Y. et al. Fine-grained attention-based phrase-aware network for aspect-level sentiment analysis. Artif Intell Rev 55, 3727–3746 (2022). https://doi.org/10.1007/s10462-021-10080-6

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