Abstract
Aspect-based sentiment analysis is an important natural language processing task that allows to extract the sentiment expressed in a review for parts or aspects of a product or service. Extracting all aspects for a domain without manual rules or annotations is a major challenge. In this paper, we propose a method for this task based on a Convolutional Neural Network (CNN) and two embedding layers. We address shortcomings of state-of-the-art methods by combining a CNN with an embedding layer trained on the general domain and one trained the specific domain of the reviews to be analysed. We evaluated our system on two SemEval datasets and compared against state-of-the-art methods that have been evaluated on the same data. The results indicate that our system performs comparably well or better than more complex systems that may take longer to train.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brun, C., Popa, D.N., Roux, C.: XRCE: hybrid classification for aspect-based sentiment analysis. In: Proceedings of SemEval, pp. 838–842 (2014)
Chernyshevich, M.: IHS R&D belarus: cross-domain extraction of product features using CRF. In: Proceedings of SemEval, pp. 309–313 (2014)
Cuong, N.V., Ye, N., Lee, W.S., et al.: Conditional random field with high-order dependencies for sequence labeling and segmentation. J. Mach. Learn. Res. 15(1), 981–1009 (2014)
He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of WWW, pp. 507–517 (2016)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of SIGKDD, pp. 168–177. ACM (2004)
Jakob, N., Gurevych, I.: Extracting opinion targets in a single-and cross-domain setting with conditional random fields. In: Proceedings of EMLNP, pp. 1035–1045 (2010)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Li, X., Lam, W.: Deep multi-task learning for aspect term extraction with memory interaction. In: Proceedings of EMNLP, pp. 2886–2892 (2017)
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Liu, P., Joty, S., Meng, H.: Fine-grained opinion mining with recurrent neural networks and word embeddings. In: Proceedings of EMNLP, pp. 1433–1443 (2015)
Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. arXiv preprint: arXiv:1301.3781 (2013)
Okazaki, N.: CRFsuite: a fast implementation of conditional random fields (CRFs) (2007). www.chokkan.org/software/crfsuite
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of EMNLP, pp. 1532–1543 (2014)
Pontiki, M., Galanis, D., Papageorgiou, H., et al.: SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of SemEval, pp. 19–30 (2016)
Pontiki, M., Galanis, D., Pavlopoulos, J., et al.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of SemEval, pp. 27–35 (2014)
Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl. Based Syst. 108, 42–49 (2016)
Sarawagi, S., Cohen, W.: Semi-markov conditional random fields for information extraction. In: Proceedings of NIPS, pp. 1185–1192 (2005)
Toh, Z., Su, J.: Nlangp at SemEval-2016 task 5: Improving aspect based sentiment analysis using neural network features. In: Proceedings of SemEval, pp. 282–288 (2016)
Wang, W., Pan, S.J., Dahlmeier, D., et al.: Recursive neural conditional random fields for aspect-based sentiment analysis. arXiv preprint: arXiv:1603.06679 (2016)
Xenos, D., Theodorakakos, P., Pavlopoulos, J., et al.: Aueb-absa at SemEval-2016 task 5: ensembles of classifiers and embeddings for aspect based sentiment analysis. In: Proceedings of SemEval, pp. 312–317 (2016)
Yin, Y., Wei, F., Dong, L., et al.: Unsupervised word and dependency path embeddings for aspect term extraction. arXiv preprint: arXiv:1605.07843 (2016)
Zhiqiang, T., Wenting, W.: DLIREC: aspect term extraction and term polarity classification system. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 235–240 (2014)
Zhuang, L., Jing, F., Zhu, X.Y.: Movie review mining and summarization. In: Proceedings of CIKM, pp. 43–50. ACM (2006)
Acknowledgment
This research work is part of the TYPHON Project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 780251.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Barnaghi, P., Kontonatsios, G., Bessis, N., Korkontzelos, Y. (2019). Aspect Extraction from Reviews Using Convolutional Neural Networks and Embeddings. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-23281-8_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23280-1
Online ISBN: 978-3-030-23281-8
eBook Packages: Computer ScienceComputer Science (R0)