Abstract
Many studies on aspect-based sentiment analysis (ABSA) aim to directly predict aspects and polarities at sentence level. However, it is not rare that a long sentence expresses multiple aspects. In this paper, we propose to study ABSA at EDU-level. Elementary discourse unit (EDU) in rhetorical structure theory is an atomic semantic unit, similar to a clause in a sentence. Through manual annotation of 8,823 EDUs, obtained from the SemEval-2014 Task 4 Restaurant Review dataset, we show that more than 97% of EDUs express at most one aspect. Based on this observation, we propose an EDU-level Capsule network for ABSA. EDU-Capsule learns EDU representations within its sentential context for aspect detection and sentiment prediction. EDU-Capsule outperforms strong baselines in our experiments on two benchmark datasets. Both the EDU-level annotations and EDU-Capsule source code are released to support further studies in this area.
Similar content being viewed by others
Notes
http://138.197.118.157:8000/segbot/ SegBot segmentation achieves an F1-Score of 92.2% on RST-DT Dataset, reported in its original paper [15].
The restaurant-2014 dataset and preprocessed Laptop dataset contain 248 (5.23%) and 32 (1.40%) aspects with conflicting sentiments, respectively (refer to Tables 2 and 4 for more detailed statistics). Some literature [8, 10] refers to sentiment prediction on the resultant positive, negative, and neutral sentiment labels as a 3-way/class classification.
References
Liu B (2012) Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers
Wang Y, Li S, Yang J (2018) Toward fast and accurate neural discourse segmentation. In: EMNLP, Brussels, Belgium, pp. 962–967
Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. In: EMNLP, Austin, Texas, USA, pp. 606–615
Xue W, Li T (2018) Aspect based sentiment analysis with gated convolutional networks. In: ACL, Melbourne, Australia, pp. 2514–2523
Li X, Bing L, Lam W, Shi B (2018) Transformation networks for target-oriented sentiment classification. In: ACL, Melbourne, Australia, pp. 946–956
Hu M, Zhao S, Zhang L, Cai K, Su Z, Cheng R, Shen X (2019) CAN: constrained attention networks for multi-aspect sentiment analysis. In: EMNLP-IJCNLP, Hong Kong, China, pp. 4600–4609
Jiang Q, Chen L, Xu R, Ao X, Yang M (2019) A challenge dataset and effective models for aspect-based sentiment analysis. In: EMNLP-IJCNLP, Hong Kong, China, pp. 6279–6284
Wang Y, Sun A, Huang M, Zhu X (2019) Aspect-level sentiment analysis using as-capsules. In: WWW, San Francisco, CA, USA, pp. 2033–2044
Sun C, Huang L, Qiu X (2019) Utilizing BERT for aspect-based sentiment analysis via constructing auxiliary sentence. In: NAACL-HLT, Minneapolis, MN, USA, pp. 380–385
Wu Z, Ong D.C (2021) Context-guided BERT for targeted aspect-based sentiment analysis. In: AAAI, Online, pp. 14094–14102
Mann W.C, Thompson S.A (1986) Rhetorical structure theory: Description and construction of text structures. marina del rey. CA: Information Sciences Institute
Galley M, McKeown K.R, Fosler-Lussier E, **g H (2003) Discourse segmentation of multi-party conversation. In: ACL, Sapporo, Japan, pp. 562–569
Misra H, Yvon F, Jose J.M, Cappé O (2009) Text segmentation via topic modeling: an analytical study. In: CIKM, Hong Kong, China, pp. 1553–1556
Cardoso P.C.F, Taboada M, Pardo T.A.S (2013) On the contribution of discourse structure to topic segmentation. In: SIGDIAL, Metz, France, pp. 92–96
Li J, Sun A, Joty S.R (2018) Segbot: A generic neural text segmentation model with pointer network. In: IJCAI, Stockholm, Sweden, pp. 4166–4172
Zhou Y (2013) Fine-grained sentiment analysis with discourse structure. PhD thesis, Master’s thesis, Saarland University, Germany
Lazaridou A, Titov I, Sporleder C (2013) A bayesian model for joint unsupervised induction of sentiment, aspect and discourse representations. In: ACL, pp. 1630–1639
Bhatia P, Ji Y, Eisenstein J (2015) Better document-level sentiment analysis from RST discourse parsing. In: EMNLP, Lisbon, Portugal, pp. 2212–2218
Ji Y, Smith N.A (2017) Neural discourse structure for text categorization. In: ACL, Vancouver, Canada, pp. 996–1005
Zhang M, Qian T (2020) Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: EMNLP, Online, pp. 3540–3549
Tang H, Ji D, Li C, Zhou Q (2020) Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: ACL, Online, pp. 6578–6588
Hu M, Liu B (2004) Mining opinion features in customer reviews. In: AAAI, San Jose, California, USA, pp. 755–760
Zhang L, Liu B, Lim S.H, O’Brien-Strain E (2010) Extracting and ranking product features in opinion documents. In: COLING, pp. 1462–1470. Chinese Information Processing Society of China, Bei**g, China
Kiritchenko S, Zhu X, Cherry C, Mohammad S.M (2014) Nrc-canada-2014: Detecting aspects and sentiment in customer reviews. In: SemEval@COLING, Dublin, Ireland, pp. 437–442
Zhou X, Wan X, **ao J (2015) Representation learning for aspect category detection in online reviews. In: AAAI, pp. 417–424. AAAI Press, Austin, Texas, USA
Xue W, Zhou W, Li T, Wang Q (2017) MTNA: A neural multi-task model for aspect category classification and aspect term extraction on restaurant reviews. In: IJCNLP, Taipei, Taiwan, pp. 151–156
Tang D, Qin B, Feng X, Liu T (2016) Effective lstms for target-dependent sentiment classification. In: COLING, Osaka, Japan, pp. 3298–3307
Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. In: IJCAI, Melbourne, Australia, pp. 4068–4074
Wang W, Pan S.J, Dahlmeier D, **ao X (2017) Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: AAAI, California, USA, pp. 3316–3322
Wang J, Li J, Li S, Kang Y, Zhang M, Si L, Zhou G (2018) Aspect sentiment classification with both word-level and clause-level attention networks. In: IJCAI, Stockholm, Sweden, pp. 4439–4445
Tay Y, Tuan L.A, Hui S.C (2018) Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis. In: AAAI, Louisiana, USA, pp. 5956–5963
Yang J, Yang R, Wang C, **e J (2018) Multi-entity aspect-based sentiment analysis with context, entity and aspect memory. In: AAAI, pp. 6029–6036. AAAI Press, Louisiana, USA
Bao L, Lambert P, Badia T (2019) Attention and lexicon regularized LSTM for aspect-based sentiment analysis. In: ACL, Florence, Italy, pp. 253–259
Wang Y, Sun A, Han J, Liu Y, Zhu X (2018) Sentiment analysis by capsules. In: WWW, Lyon, France, pp. 1165–1174
Zirn C, Niepert M, Stuckenschmidt H, Strube M (2011) Fine-grained sentiment analysis with structural features. In: IJCNLP, Chiang Mai, Thailand, pp. 336–344
Zhang Z, Singh M.P (2018) Limbic: Author-based sentiment aspect modeling regularized with word embeddings and discourse relations. In: EMNLP, Brussels, Belgium, pp. 3412–3422
Hoogervorst R, Essink E, Jansen W, van den Helder M, Schouten K, Frasincar F, Taboada M (2016) Aspect-based sentiment analysis on the web using rhetorical structure theory. In: ICWE, vol. 9671. Lugano, Switzerland, pp. 317–334
Angelidis S, Lapata M (2018) Multiple instance learning networks for fine-grained sentiment analysis. TACL 6:17–31
Li J, Chiu B, Shang S, Shao L (2020) Neural text segmentation and its application to sentiment analysis. TKDE
Yang Y, Wu B, Li L, Wang S (2020) A joint model for aspect-category sentiment analysis with textgcn and bi-gru. In: DSC, pp. 156–163. IEEE, Hong Kong, China
Li Y, Yang Z, Yin C, Pan X, Cui L, Huang Q, Wei T (2020) A joint model for aspect-category sentiment analysis with shared sentiment prediction layer. In: CCL, pp. 388–400. Springer, Hainan, China
Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) Semeval-2014 task 4: Aspect based sentiment analysis. In: SemEval@COLING, Dublin, Ireland, pp. 27–35
Li M, Zhang L, Ji H, Radke R.J (2019) Keep meeting summaries on topic: Abstractive multi-modal meeting summarization. In: ACL, Florence, Italy, pp. 2190–2196
Gao Y, Wu C, Li J, Joty S.R, Hoi S.C.H, **ong C, King I, Lyu M.R (2020) Discern: Discourse-aware entailment reasoning network for conversational machine reading. In: EMNLP, Online, pp. 2439–2449
Sabour S, Frosst N, Hinton G.E (2017) Dynamic routing between capsules. In: NIPS, CA, USA, pp. 3856–3866
Kingma D.P, Ba J (2015) Adam: A method for stochastic optimization. In: ICLR, CA, USA
Pennington J, Socher R, Manning C.D (2014) Glove: Global vectors for word representation. In: EMNLP, Doha, Qatar, pp. 1532–1543
Liu B, Hu M, Cheng J (2005) Opinion observer: analyzing and comparing opinions on the web. In: WWW, Chiba, Japan, pp. 342–351
Kumar A, Saini M, Sharan A (2020) Aspect category detection using statistical and semantic association. Comput. Intell. 36(3):1161–1182
Schouten K, van der Weijde O, Frasincar F, Dekker R (2018) Supervised and unsupervised aspect category detection for sentiment analysis with co-occurrence data. IEEE Trans. Cybern. 48(4):1263–1275
Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, Minneapolis, MN, USA, pp. 4171–4186
Carlson L, Okurowski ME, Marcu D (2002) Rst discourse treebank. Linguistic Data Consortium, University of Pennsylvania
Carlson L, Marcu D (2001) Discourse tagging reference manual. ISI Technical Report ISI-TR-545 54(2001), 56
Acknowledgements
This work was partly supported by the National Key RD Program of China (2020AAA0105200) and the National Science Foundation of China (NSFC No.62106249).
Author information
Authors and Affiliations
Corresponding authors
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Lin, T., Sun, A. & Wang, Y. EDU-Capsule: aspect-based sentiment analysis at clause level. Knowl Inf Syst 65, 517–541 (2023). https://doi.org/10.1007/s10115-022-01797-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-022-01797-z