review-article

Deep Learning for Aspect-Based Sentiment Analysis: : A Comparative Review

Authors:

Abeer AlsadoonAuthors Info & Claims

Volume 118, Issue C

Pages 272 - 299

https://doi.org/10.1016/j.eswa.2018.10.003

Published: 15 March 2019 Publication History

Highlights

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Over 40 models for aspect-based sentiment analysis are summarized and classified.

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Deep learning methods use fewer parameters but achieved comparative performance.

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Deep learning is still in infancy, given challenges in data, domains and languages.

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A task-combined and concept-centric approach should be considered in future studies.

Abstract

The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.

References

[1]

M.S. Akhtar, A. Ekbal, P. Bhattacharyya, Aspect based sentiment analysis in hindi: Resource creation and evaluation, in: Proceedings of the 10th edition of the Language Resources and Evaluation Conference (LREC), 2016.

[2]

M.S. Akhtar, D. Gupta, A. Ekbal, P. Bhattacharyya, Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis, Knowledge-Based Systems 125 (2017) 116–135. https://doi.org/10.1016/j.knosys.2017.03.020.

Digital Library

[3]

M.S. Akhtar, A. Kumar, A. Ekbal, P. Bhattacharyya, A hybrid deep learning architecture for sentiment analysis, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp. 482–493. Retrieved from http://aclweb.org/anthology/C16-1047.

[4]

M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, B. Gupta, Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews, Journal of Computational Science (2017) https://doi.org/10.1016/j.jocs.2017.11.006.

[5]

M.H. Alam, W.J. Ryu, S.K. Lee, Joint multi-grain topic sentiment: Modeling semantic aspects for online reviews, Information Sciences 339 (2016) 206–223. https://doi.org/10.1016/j.ins.2016.01.013.

[6]

T. Àlvarez-López, J. Juncal-Martínez, M. Fernández-Gavilanes, E. Costa-Montenegro, J.F. González-Castaño, GTI at SemEval-2016 Task 5: SVM and CRF for aspect detection and unsupervised aspect-based sentiment analysis, in: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016, pp. 306–311. Retrieved from http://aclweb.org/anthology/S16-1049.

[7]

O. Araque, I. Corcuera-Platas, J.F. Sánchez-Rada, C.A. Iglesias, Enhancing deep learning sentiment analysis with ensemble techniques in social applications, Expert Systems with Applications 77 (2017) 236–246. https://doi.org/10.1016/j.eswa.2017.02.002.

Digital Library

[8]

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

[9]

Y. Bengio, H. Schwenk, J.S. Senécal, F. Morin, J.L. Gauvain, Neural probabilistic language models, Studies in Fuzziness and Soft Computing 194 (2006) 137–186. https://doi.org/10.1007/10985687_6.

[10]

C. Brun, J. Perez, C. Roux, XRCE at SemEval-2016 Task 5: Feedbacked ensemble modelling on syntactico-semantic knowledge for aspect based sentiment analysis, in: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), 2016, pp. 282–286.

[11]

E. Cambria, S. Poria, A. Gelbukh, M. Thelwall, Sentiment analysis is a big suitcase, IEEE Intelligent Systems 32 (6) (2017) 74–80. https://doi.org/10.1109/MIS.2017.4531228.

[12]

F.S. Çetin, E. Yıldırım, C. Özbey, G. Eryiğit, TGB at SemEval-2016 Task 5 : Multi-lingual constraint system for as- pect based sentiment analysis, in: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval 2016), (Slot 1), 2016, pp. 27–35. Retrieved from http://link.springer.com/10.1007/978-3-642-19460-3.

[13]

A. Chaudhuri, S.K. Ghosh, Sentiment analysis of customer reviews using robust hierarchical bidirectional recurrent neural network, R. Silhavy, R. Senkerik, Z.K. Oplatkova, P. Silhavy, Z. Prokopova (Eds.), Advances in Intelligent Systems and Computing, 464, 2016, https://doi.org/10.1007/978-3-319-33625-1.

[14]

P. Chen, Z. Sun, L. Bing, W. Yang, Recurrent attention network on memory for aspect sentiment analysis, Emnlp (2017) 452–461. Retrieved from http://aclweb.org/anthology/D17-1047.

[15]

P. Chen, B. Xu, M. Yang, S. Li, Clause sentiment identification based on convolutional neural network with context embedding, in: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016, 2016, pp. 1532–1538. https://doi.org/10.1109/FSKD.2016.7603403.

[16]

T. Chen, R. Xu, Y. He, X. Wang, Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN, Expert Systems with Applications 72 (2017) 221–230. https://doi.org/10.1016/j.eswa.2016.10.065.

Digital Library

[17]

J. Cheng, S. Zhao, J. Zhang, I. King, X. Zhang, H. Wang, Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network, in: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM ’17, 2017, (pp. 97–106). https://doi.org/10.1145/3132847.3133037.

[18]

M. Chernyshevich, IHS R & D Belarus : Cross-domain extraction of product features using conditional random fields, Semeval, (SemEval) (2014) 309–313.

[19]

Chiu, J.P.C., & Nichols, E. (2015). Named entity recognition with bidirectional LSTM-CNNs. ArXiv:1511.08308, (2003). Retrieved from http://arxiv.org/abs/1511.08308.

[20]

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. ArXiv:1406.1078. https://doi.org/10.3115/v1/D14-1179.

[21]

J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, in: NIPS 2014 Deep Learning and Representation Learning Workshop, 2014, pp. 1–9. ArXiv:1412.3555.

[22]

R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, P. Kuksa, Natural language processing (Almost) from scratch, Journal of Machine Learning Research 12 (2011) 2493–2537. https://doi.org/10.1.1.231.4614.

[23]

O. De Clercq, The many aspects of fine-grained sentiment analysis: An overview of the task and its main challenges, in: HUSO 2016, 2016, pp. 23–28. Retrieved from https://biblio.ugent.be/publication/8500953/file/8500962.

[24]

Y. Ding, C. Yu, J. Jiang, A neural network model for semi-supervised review aspect identification, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10235, 2017, pp. 668–680. LNAI https://doi.org/10.1007/978-3-319-57529-2_52.

[25]

Y. Ding, J. Yu, J. Jiang, Recurrent neural networks with auxiliary labels for cross-domain opinion target extraction, Aaai (2017) 3436–3442.

[26]

Do, Q.N.T., Bethard, S., & Moens, M.-F. (2017). Improving implicit semantic role labeling by predicting semantic frame arguments. ArXiv:1704.02709. Retrieved from http://arxiv.org/abs/1704.02709.

[27]

L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, K. Xu, Adaptive recursive neural network for target-dependent twitter sentiment classification, Acl-2014 (2014) 49–54.

[28]

M. Dragoni, G. Petrucci, A neural word embeddings approach for multi-domain sentiment analysis, IEEE Transactions on Affective Computing 8 (4) (2017) 457–470. https://doi.org/10.1109/TAFFC.2017.2717879.

[29]

H. Du, X. Xu, X. Cheng, D. Wu, Y. Liu, Z. Yu, Aspect-specific sentimental word embedding for sentiment analysis of online reviews, International Conference Companion on World Wide Web (2016) 29–30. https://doi.org/10.1145/2872518.2889403.

[30]

M. El-Masri, N. Altrabsheh, H. Mansour, Successes and challenges of Arabic sentiment analysis research: a literature review, Social Network Analysis and Mining (2017) https://doi.org/10.1007/s13278-017-0474-x.

[31]

J.L. Elman, Distributed representations, simple recurrent networks, and grammatical structure, Machine Learning 7 (2) (1991) 195–225. https://doi.org/10.1023/A:1022699029236.

[32]

Y. Fan, Y. Qian, F. Xie, F.K. Soong, TTS synthesis with bidirectional LSTM based recurrent neural networks, in: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2014, pp. 1964–1968.

[33]

J. Feng, S. Cai, X. Ma, Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm, Cluster Computing (2018) 1–19. https://doi.org/10.1007/s10586-017-1626-5.

[34]

M. Fernández-Gavilanes, T. Álvarez-López, J. Juncal-Martínez, E. Costa-Montenegro, F. Javier González-Castaño, Unsupervised method for sentiment analysis in online texts, Expert Systems with Applications 58 (2016) 57–75. https://doi.org/10.1016/j.eswa.2016.03.031.

Digital Library

[35]

A. García-Pablos, M. Cuadros, G. Rigau, W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis, Expert Systems with Applications 91 (2018) 127–137. https://doi.org/10.1016/j.eswa.2017.08.049.

[36]

A. Giachanou, F. Crestani, Like it or not: A survey of Twitter sentiment analysis methods, ACM Comput Surv 49 (2) (2016) 1–41. Article 28 https://doi.org/10.1145/2938640.

[37]

Y. Goldberg, A primer on neural network models for natural language processing, Journal of Artificial Intelligence Research 57 (2016) 345–420.

[38]

Y. Goldberg, Neural network methods for natural language processing, Synthesis Lectures on Human Language Technologies 10 (1) (2017) 1–309. https://doi.org/10.2200/S00762ED1V01Y201703HLT037.

[39]

I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, Nature 521 (7553) (2016) 800. https://doi.org/10.1038/nmeth.3707.

[40]

E. Grave, P. Bojanowski, P. Gupta, A. Joulin, T. Mikolov, Learning word vectors for 157 languages, in: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018), 2018, Retrieved from http://arxiv.org/abs/1802.06893.

[41]

A. Graves, Supervised sequence labelling with recurrent neural networks, ArXiv:1308.0850 12 (1) (2008) 126–140. https://doi.org/10.1145/2911451.2926729.

[42]

X. Gu, Y. Gu, H. Wu, Cascaded Convolutional neural networks for aspect-based opinion summary, Neural Processing Letters 46 (2) (2017) 581–594. https://doi.org/10.1007/s11063-017-9605-7.

[43]

A. Hassan, A. Mahmood, Deep learning approach for sentiment analysis of short texts, in: 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), 2017, pp. 705–710. (September) https://doi.org/10.1109/ICCAR.2017.7942788.

[44]

S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation 9 (8) (1997) 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735.

Digital Library

[45]

M. Hu, B. Liu, Mining and summarizing customer reviews, in: Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’04, 2004, p. 168. https://doi.org/10.1145/1014052.1014073.

[46]

O. Irsoy, C. Cardie, Opinion mining with deep recurrent neural networks, Emnlp-2014 (2014) 720–728. https://doi.org/10.3115/v1/D14-1080.

[47]

S. Jebbara, P. Cimiano, Aspect-based sentiment analysis using a two-step neural network architecture, Semantic Web Evaluation Challenge (2016) 153–167. https://doi.org/10.3233/978-1-61499-672-9-1123.

[48]

Jebbara, S., & Cimiano, P. (2017). Aspect-based relational sentiment analysis using a stacked neural network architecture. ArXiv:1709.06309. https://doi.org/10.3233/978-1-61499-672-9-1123.

[49]

S.M. Jiménez-Zafra, M.T. Martín-Valdivia, E. Martínez-Cámara, L.A. Ureña-López, Combining resources to improve unsupervised sentiment analysis at aspect-level, Journal of Information Science 42 (2) (2016) 213–229. https://doi.org/10.1177/0165551515593686.

[50]

J.S. Kessler, M. Eckert, L. Clark, N. Nicolov, The ICWSM 2010 JDPA sentiment corpus for the automotive domain, in: 4th Int'l AAAI Conference on Weblogs and Social Media Data Workshop Challenge (ICWSM-DWC 2010), 2010, Retrieved from http://www.cs.indiana.edu/∼jaskessl/icwsm10.pdf.

[51]

Y. Kim, Convolutional neural networks for sentence classification, Arxiv (2014) 23–31. https://doi.org/10.1145/1599272.1599278.

[52]

S. Kiritchenko, X. Zhu, C. Cherry, S. Mohammad, NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews, in: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 437–442. https://doi.org/10.3115/v1/S14-2076.

[53]

A. Kumar, S. Kohail, A. Kumar, A. Ekbal, C. Biemann, IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: combining domain dependency and distributional semantics features for aspect based sentiment analysis, in: Proceedings of the 10th International Workshop on Semantic Evaluation, (SemEval), 2016, pp. 1129–1135.

[54]

H. Lakkaraju, R. Socher, C.D. Manning, Aspect specific sentiment analysis using hierarchical deep learning, NIPS WS on Deep Neural Networks and Representation Learning (2014) 1–9.

[55]

Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural architectures for named entity recognition. ArXiv:1603.01360. https://doi.org/10.18653/v1/N16-1030.

[56]

Y. Lecun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015) 436–444. https://doi.org/10.1038/nature14539.

[57]

Li, X., Bing, L., Li, P., Lam, W., & Yang, Z. (2018). Aspect term extraction with history attention and selective transformation. ArXiv:1805.00760. Retrieved from http://arxiv.org/abs/1805.00760.

[58]

Li, X.; Lam, W. (2017) : Retrieved from http://aclweb.org/anthology/D/D17/D17-1310.pdf.

[59]

B. Liu, Sentiment analysis: Mining opinions, sentiments, and emotions. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (2015) https://doi.org/10.1017/CBO9781139084789.

[60]

P. Liu, S. Joty, H. Meng, Fine-grained opinion mining with recurrent neural networks and word embeddings, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 1433–1443. Retrieved from http://aclweb.org/anthology/D15-1168.

[61]

S.L. Lo, E. Cambria, R. Chiong, D. Cornforth, Multilingual sentiment analysis: from formal to informal and scarce resource languages, Artificial Intelligence Review 48 (4) (2017) 499–527. https://doi.org/10.1007/s10462-016-9508-4.

[62]

D. Ma, S. Li, X. Zhang, H. Wang, Interactive attention networks for aspect-level sentiment classification, in: IJCAI International Joint Conference on Artificial Intelligence, 2017, pp. 4068–4074.

[63]

R. Ma, K. Wang, T. Qiu, A.K. Sangaiah, D. Lin, H.Bin Liaqat, Feature-based compositing memory networks for aspect-based sentiment classification in social internet of things, Future Generation Computer Systems (2017) https://doi.org/10.1016/j.future.2017.11.036.

[64]

Ma, X., & Hovy, E. (2016). End-to-end sequence labeling via Bi-directional LSTM-CNNs-CRF. ArXiv:1603.01354. https://doi.org/10.18653/v1/P16-1101.

[65]

Y. Ma, H. Peng, E. Cambria, Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM, Aaai (2018) Retrieved from http://sentic.net/sentic-lstm.pdf.

[66]

L. Mai, B. Le, Aspect-based sentiment analysis of vietnamese texts with deep learning, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 10751, 2018, pp. 149–158. LNAI https://doi.org/10.1007/978-3-319-75417-8_14.

[67]

M. Maia, S. Handschuh, A. Freitas, B. Davis, R. McDermott, M. Zarrouk, A. Balahur, WWW’18 Open Challenge: Financial opinion mining and question answering, WWW ’18 Companion: The 2018 Web Conference Companion, April 23–27, 2018, ACM, New York, NY, USA, 2018, Lyon, France.

[68]

A.S. Manek, P.D. Shenoy, M.C. Mohan, K.R. Venugopal, Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier, World Wide Web 20 (2) (2017) 135–154. https://doi.org/10.1007/s11280-015-0381-x.

Digital Library

[69]

Marcheggiani, D., Frolov, A., & Titov, I. (2017). A simple and accurate syntax-agnostic neural model for dependency-based semantic role labeling. ArXiv:1701.02593. Retrieved from http://arxiv.org/abs/1701.02593.

[70]

T. Mikolov, G. Corrado, K. Chen, J. Dean, Efficient estimation of word representations in vector space, in: Proceedings of the International Conference on Learning Representations (ICLR 2013), 2013, pp. 1–12. https://doi.org/10.1162/153244303322533223.

[71]

T.H. Nguyen, K. Shirai, PhraseRNN: Phrase recursive neural network for aspect-based sentiment analysis, in: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015, pp. 2509–2514. (September) Retrieved from http://aclweb.org/anthology/D15-1298.

[72]

B. Pang, L. Lee, Opinion mining and sentiment analysis. Foundations and Trends® in InformatioPang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis, Foundations and Trends® in Information Retrieval 1 (2) (2008) 91–231,. Retrieval, 1(2), 91–231 https://doi.org/10.1561/1500000001.

[73]

V. Parkhe, B. Biswas, Sentiment analysis of movie reviews: finding most important movie aspects using driving factors, Soft Computing 20 (9) (2016) 3373–3379. https://doi.org/10.1007/s00500-015-1779-1.

[74]

H. Peng, E. Cambria, A. Hussain, A review of sentiment analysis research in Chinese Language, Cognitive Computation (2017) https://doi.org/10.1007/s12559-017-9470-8.

[75]

H. Peng, Y. Ma, Y. Li, E. Cambria, Learning multi-grained aspect target sequence for Chinese sentiment analysis, Knowledge-Based Systems 148 (2018) 55–65. https://doi.org/10.1016/j.knosys.2018.02.034.

[76]

J. Pennington, R. Socher, C. Manning, Glove: Global vectors for word representation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1532–1543. https://doi.org/10.3115/v1/D14-1162.

[77]

R. Piryani, V. Gupta, V.K. Singh, Movie Prism: A novel system for aspect level sentiment profiling of movies, Journal of Intelligent and Fuzzy Systems (2017) https://doi.org/10.3233/JIFS-169272.

[78]

Plank, B., Søgaard, A., & Goldberg, Y. (2016). Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss. ArXiv:1604.05529. https://doi.org/10.18653/v1/P16-2067.

[79]

M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, M. AL-Smadi, G. Eryi\ugit, SemEval-2016 Task 5: Aspect based sentiment analysis, in: Proceedings of the 10th International Workshop on Semantic Evaluation, 2016, pp. 19–30. https://doi.org/10.18653/v1/S16-1055.

[80]

M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, I. Androutsopoulos, SemEval-2015 Task 12: Aspect based sentiment analysis, in: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, 2015, pp. 486–495. https://doi.org/10.1007/978-3-642-40837-3.

[81]

M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, SemEval-2014 Task 4: Aspect based sentiment analysis, Proceedings of the 8th International Workshop on Semantic Evaluation (2014) 27–35. (SemEval) https://doi.org/10.15520/ajcsit.v4i8.9.

[82]

S. Poria, E. Cambria, A. Gelbukh, Aspect extraction for opinion miningwith a deep convolutional neural network, Knowledge-Based Systems 108 (2016) 42–49. https://doi.org/10.1016/j.knosys.2016.06.009.

[83]

S. Poria, E. Cambria, A. Gelbukh, F. Bisio, A. Hussain, Sentiment data flow analysis by means of dynamic linguistic patterns, IEEE Computational Intelligence Magazine 10 (4) (2015) 26–36. https://doi.org/10.1109/MCI.2015.2471215.

[84]

S. Poria, I. Chaturvedi, E. Cambria, F. Bisio, Sentic LDA: Improving on LDA with semantic similarity for aspect-based sentiment analysis, in: Proceedings of the International Joint Conference on Neural Networks, 2016, pp. 4465–4473. 2016–Octob https://doi.org/10.1109/IJCNN.2016.7727784.

[85]

T.A. Rana, Y.-N. Cheah, Aspect extraction in sentiment analysis : comparative analysis and survey, Artificial Intelligence Review 46 (4) (2016) 459–483. https://doi.org/10.1007/s10462-016-9472-z.

[86]

L.M. Rojas-Barahona, Deep learning for sentiment analysis, Language and Linguistics Compass 10 (12) (2016) 701–719. https://doi.org/10.1111/lnc3.12228.

[87]

S. Ruder, P. Ghaffari, J.G. Breslin, INSIGHT-1 at SemEval-2016 Task 5: Deep learning for multilingual aspect-based sentiment analysis, in: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016, Retrieved from http://arxiv.org/abs/1609.02748.

[88]

Saeidi, M., Bouchard, G., Liakata, M., & Riedel, S. (2016). SentiHood: Targeted aspect based sentiment analysis dataset for urban neighbourhoods. ArXiv:1610.03771. Retrieved from http://arxiv.org/abs/1610.03771.

[89]

J. Saias, Sentiue: target and aspect based sentiment analysis in SemEval-2015 Task 12, in: Proceedings of the 9th International Workshop on Semantic Evaluation, (SemEval), 2015, pp. 767–771.

[90]

J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks (2015) https://doi.org/10.1016/j.neunet.2014.09.003.

[91]

K. Schouten, F. Frasincar, Survey on aspect-level sentiment analysis, IEEE Transactions on Knowledge and Data Engineering 28 (3) (2016) 813–830. https://doi.org/10.1109/TKDE.2015.2485209.

Digital Library

[92]

Y. Shen, H. Yun, Z.C. Lipton, Y. Kronrod, A. Anandkumar, Deep active learning for named entity recognition (2017) 1–15. ArXiv:1707.05928. https://doi.org/10.18653/v3.

[93]

R. Socher, A. Perelygin, J. Wu, Recursive deep models for semantic compositionality over a sentiment treebank, in: Proceedings of the …, 2013, pp. 1631–1642. https://doi.org/10.1371/journal.pone.0073791.

[94]

E. Strubell, P. Verga, D. Belanger, A. McCallum, Fast and accurate sequence labeling with iterated dilated convolutions, in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, Retrieved from http://arxiv.org/abs/1702.02098.

[95]

S. Sukhbaatar, A. Szlam, J. Weston, R. Fergus, End-to-end memory networks, in: Advances in neural information processing systems, 2015, pp. 2440–2448. https://doi.org/v5.

[96]

C. Sutton, An introduction to conditional random fields, Foundations and Trends® in Machine Learning 4 (4) (2012) 267–373. https://doi.org/10.1561/2200000013.

[97]

K.S. Tai, R. Socher, C.D. Manning, Improved semantic representations from tree-structured long short-term memory networks, in: Proceedings of ACL, 2015, pp. 1556–1566. https://doi.org/10.1515/popets-2015-0023.

[98]

A. Tamchyna, K. Veselovská, UFAL at SemEval-2016 Task 5: Recurrent neural networks for sentence classification, in: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016, pp. 367–371. Retrieved from https://aclweb.org/anthology/S/S16/S16-1059.pdf.

[99]

D. Tang, B. Qin, X. Feng, T. Liu, Effective LSTMs for target-dependent sentiment classification, in: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2015, Retrieved from http://arxiv.org/abs/1512.01100.

[100]

Tang, D., Qin, B., & Liu, T. (2016). Aspect level sentiment classification with deep memory network. ArXiv:1605.08900. Retrieved from http://arxiv.org/abs/1605.08900.

[101]

D. Tang, F. Wei, B. Qin, N. Yang, T. Liu, M. Zhou, Sentiment embeddings with applications to sentiment analysis, in: IEEE Transactions on Knowledge and Data Engineering, 28, 2016, pp. 496–509. https://doi.org/10.1109/TKDE.2015.2489653.

[102]

D. Tang, F. Wei, N. Yang, M. Zhou, T. Liu, B. Qin, Learning sentiment-specific word embedding, Acl (2014) 1555–1565. https://doi.org/10.3115/1220575.1220648.

[103]

Tay, Y., Luu, A.T., & Hui, S.C. (2017). Learning to attend via word-aspect associative fusion for aspect-based sentiment analysis. ArXiv:1712.05403. Retrieved from http://arxiv.org/abs/1712.05403.

[104]

Y. Tay, L.A. Tuan, S.C. Hui, Dyadic memory networks for aspect-based sentiment analysis, in: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM ’17, 2017, pp. 107–116. https://doi.org/10.1145/3132847.3132936.

[105]

J. Thanaki, Python Natural Language Processing (2017) Packt Publishing Ltd.

[106]

Z. Toh, J. Su, NLANGP: supervised machine learning system for aspect category classification and opinion target extraction, in: Proceedings of the 9th International Workshop on Semantic Evaluation, 14(SemEval), 2015, pp. 496–501.

[107]

Z. Toh, J. Su, NLANGP at SemEval-2016 Task 5: Improving aspect based sentiment analysis using neural network features, in: Proceedings of SemEval-2016, 2015(Subtask 1), 2016, pp. 282–288. Retrieved from http://www.aclweb.org/anthology/S16-1045.

[108]

Z. Toh, W. Wang, DLIREC: Aspect term extraction and term polarity classification system, in: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), (SemEval), 2014, pp. 235–240.

[109]

C. Toprak, N. Jakob, I. Gurevych, Sentence and expression level annotation of opinions in user-generated discourse, in: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 1(July), 2010, pp. 575–584. Retrieved from http://www.aclweb.org/anthology/P10-1059.

[110]

Z. Tu, Z. Lu, Y. Liu, X. Liu, H. Li, Coverage-based neural machine translation, Arxiv (2016) 1–19. https://doi.org/10.1145/2856767.2856776.

[111]

E. Tutubalina, S. Nikolenko, Combination of deep recurrent neural networks and conditional random fields for extracting adverse drug reactions from user reviews, Journal of Healthcare Engineering (2017) 2017 https://doi.org/10.1155/2017/9451342.

[112]

Vicente, I.S., Saralegi, X., & Agerri, R. (2017). EliXa: A modular and flexible ABSA platform. ArXiv:1702.01944. Retrieved from http://arxiv.org/abs/1702.01944.

[113]

D.T. Vo, Y. Zhang, Target-dependent twitter sentiment classification with rich automatic features, in: IJCAI International Joint Conference on Artificial Intelligence, 2015, pp. 1347–1353. Vol. 2015–Janua.

[114]

J. Wagner, P. Arora, S. Cortes, U. Barman, D. Bogdanova, J. Foster, L. Tounsi, DCU: Aspect-based polarity classification for SemEval Task 4, in: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 223–229.

[115]

B. Wang, M. Liakata, A. Zubiaga, R. Procter, TDParse : Multi-target-specific sentiment recognition on Twitter, in: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, Vol. 1, 2017, pp. 483–493. Retrieved from http://www.aclweb.org/anthology/E/E17/E17-1046.pdf.

[116]

H. Wang, Y. Lu, C. Zhai, Latent aspect rating analysis without aspect keyword supervision, in: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11, 2011, p. 618. https://doi.org/10.1145/2020408.2020505.

[117]

W. Wang, S.J. Pan, D. Dahlmeier, Coupled multi-layer attentions for co-extraction of aspect and opinion terms, Aaai (2017) 3316–3322. Retrieved from http://arxiv.org/abs/1702.01776.

[118]

W. Wang, S.J. Pan, D. Dahlmeier, X. Xiao, Recursive neural conditional random fields for aspect-based sentiment analysis, in: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16), 2016, pp. 616–626.

[119]

X. Wang, Y. Liu, C. Sun, M. Liu, X. Wang, Extended dependency-based word embeddings for aspect extraction, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9950, 2016, pp. 104–111. LNCS https://doi.org/10.1007/978-3-319-46681-1_13.

[120]

Y. Wang, M. Huang, L. Zhao, X. Zhu, Attention-based LSTM for aspect-level sentiment classification, in: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2016, pp. 606–615.

[121]

A. Weichselbraun, S. Gindl, F. Fischer, S. Vakulenko, A. Scharl, Aspect-based extraction and analysis of affective knowledge from social media streams, IEEE Intelligent Systems 32 (3) (2017) 80–88. https://doi.org/10.1109/MIS.2017.57.

Digital Library

[122]

H. Wu, Y. Gu, S. Sun, X. Gu, Aspect-based opinion summarization with convolutional neural networks, in: Proceedings of the International Joint Conference on Neural Networks, Vol. 2016, 2016, pp. 3157–3163. Octob https://doi.org/10.1109/IJCNN.2016.7727602.

[123]

L. Xu, J. Lin, L. Wang, C. Yin, J. Wang, Deep convolutional neural network based approach for aspect-based sentiment analysis, Advanced Science and Technology Letters 143 (2017) 199–204. Ast) https://doi.org/10.14257/astl.2017.143.41.

[124]

L. Xu, J. Liu, L. Wang, C. Yin, Aspect based sentiment analysis for online reviews, in: J. Park, V. Loia, G. Yi, Y. Sung (Eds.), Advances in Computer Science and Ubiquitous Computing (Lecture No, Vol. 474), Singapore, Springer, 2018.

[125]

W. Xue, W. Zhou, T. Li, Q. Wang, MTNA: A neural multi-task model for aspect category classification and aspect term extraction on restaurant reviews, in: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 2, 2017, pp. 151–156.

[126]

Yang, Z., Salakhutdinov, R., & Cohen, W. (2016). Multi-task cross-lingual sequence tagging from scratch. ArXiv:1603.06270. Retrieved from http://arxiv.org/abs/1603.06270.

[127]

H. Ye, Z. Yan, Z. Luo, W. Chao, Dependency-tree based convolutional neural networks for aspect term extraction, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10235, 2017, pp. 350–362. LNAI https://doi.org/10.1007/978-3-319-57529-2_28.

[128]

Yin, W., Kann, K., Yu, M., & Schütze, H. (2017). Comparative Study of CNN and RNN for Natural Language Processing. ArXiv:1708.02709. Retrieved from http://arxiv.org/abs/1702.01923.

[129]

T. Young, D. Hazarika, S. Poria, E. Cambria, Recent Trends in Deep Learning Based Natural Language Processing, Arxiv (2017) 1–24. Retrieved from http://arxiv.org/abs/1708.02709.

[130]

J. Yuan, Y. Zhao, B. Qin, T. Liu, Local contexts are effective for neural aspect extraction, in: Communications in Computer and Information Science, Vol. 774, 2017, pp. 244–255. https://doi.org/10.1007/978-981-10-6805-8_20.

[131]

N. Zainuddin, A. Selamat, R. Ibrahim, Hybrid sentiment classification on twitter aspect-based sentiment analysis, Applied Intelligence 48 (5) (2018) 1218–1232. https://doi.org/10.1007/s10489-017-1098-6.

[132]

W. Zaremba, I. Sutskever, Learning to Execute (2014) 1–25. ArXiv:1410.4615. https://doi.org/10.1016/S0893-6080(96)00073-1.

[133]

L. Zhang, S. Wang, B. Liu, Deep learning for sentiment analysis: A survey, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (2018) https://doi.org/10.1002/widm.1253.

[134]

M. Zhang, Y. Zhang, D. Vo, Gated Neural Networks for Targeted Sentiment Analysis, in: Proceedings of the 30th Conference on Artificial Intelligence (AAAI 2016), 2016, pp. 3087–3093. Retrieved from http://zhangmeishan.github.io/targeted-sentiment.pdf.

[135]

W. Zhao, Z. Guan, L. Chen, X. He, D. Cai, B. Wang, Q. Wang, Weakly-supervised Deep Embedding for Product Review Sentiment Analysis, IEEE Transactions on Knowledge and Data Engineering (2017) https://doi.org/10.1109/TKDE.2017.2756658.

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Index Terms

Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks
2. Information systems

Index terms have been assigned to the content through auto-classification.

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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 118, Issue C

Mar 2019

640 pages

ISSN:0957-4174

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Zhang XZhou JQi G(2025)Multimodal temporal context network for tracking dynamic changes in emotionThe Journal of Supercomputing10.1007/s11227-024-06484-081:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s11227-024-06484-0
Pan T(2024)Cyberbullying detection based on aspect-level sentiment analysisProceedings of the 2024 3rd International Conference on Cryptography, Network Security and Communication Technology10.1145/3673277.3673312(200-204)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3673277.3673312
Kanev GMladenova TValova I(2024)Analyzing Methods for Classification of Electronic Word-of-Mouth: A ReviewProceedings of the Cognitive Models and Artificial Intelligence Conference10.1145/3660853.3660872(85-89)Online publication date: 25-May-2024
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Zhao XPeng HDai QBai XPeng HLiu YGuo QYu PAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)RDGCN: Reinforced Dependency Graph Convolutional Network for Aspect-based Sentiment AnalysisProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635775(976-984)Online publication date: 4-Mar-2024
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Ahmad KLiu QKhalil MGan YKhan ALiu XLang J(2024)Aspect-specific Parsimonious Segmentation via Attention-based Graph Convolutional Network for Aspect-Based Sentiment AnalysisKnowledge-Based Systems10.1016/j.knosys.2024.112169300:COnline publication date: 18-Nov-2024
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Mao YLiu QZhang Y(2024)Sentiment analysis methods, applications, and challengesJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2024.10204836:4Online publication date: 1-Apr-2024
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Babaali MFatemi AAli Nematbakhsh M(2024)Aspect extraction with enriching word representation and post-processing rulesExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124174252:PAOnline publication date: 24-Jul-2024
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