Abstract
Opinions are key influencers of almost all human practices. One can easily find a number of opinions about any product or services in the form of product reviews. These product reviews are available in a tremendous amount. It is not feasible or even impossible to go through each review and make a concise decision about any product. Aspect-based sentiment analysis (ABSA) comes as a solution to this problem. It gives an approach to examine online reviews and provides a summary based on these reviews. Machine learning techniques have been broadly utilized for ABSA. Recently with the evolution of processing power of computers and digitization of the society, deep learning is taking off. Deep learning methods produced state-of-the-art results in various NLP tasks without intensive feature engineering. In this chapter, we present an introduction about ABSA following a comprehensive overview of various deep learning models used in the field of ABSA.
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References
Al-Smadi, Mohammad, Bashar Talafha, Mahmoud Al-Ayyoub, and Yaser Jararweh. 2018. Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. International Journal of Machine Learning and Cybernetics, pp. 1–13
Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473
Chen, Peng, Zhongqian Sun, Lidong Bing, and Wei Yang. 2017. Recurrent attention network on memory for aspect sentiment analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 452–461
Chen, Tao, Ruifeng Xu, Yulan He, Xuan Wang. 2017. Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications 72, 221–230
Dong, Li, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 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), vol. 2, 49–54
Feng, Jinzhan, Shuqin Cai, and Xiaomeng Ma. 2018. Enhanced sentiment labeling and implicit aspect identification by integration of deep convolution neural network and sequential algorithm. Cluster Computing, 1–19
Giannakopoulos, Athanasios, Claudiu Musat, Andreea Hossmann, and Michael Baeriswyl. 2017. Unsupervised aspect term extraction with b-lstm & crf using automatically labelled datasets. arXiv preprint arXiv:1709.05094
Graves, Alex, Schmidhuber, Jürgen: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18(5–6), 602–610 (2005)
Gu, Shuqin, Lipeng Zhang, Yuexian Hou, and Yin Song. 2018. A position-aware bidirectional attention network for aspect-level sentiment analysis. In Proceedings of the 27th International Conference on Computational Linguistics, 774–784
Güngör, Onur, Güngör, Tunga, Üsküdarli, Suzan: The effect of morphology in named entity recognition with sequence tagging. Natural Language Engineering 25(1), 147–169 (2019)
He, Ruidan, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018. Effective attention modeling for aspect-level sentiment classification. In Proceedings of the 27th International Conference on Computational Linguistics, 1121–1131
Hochreiter, Sepp, Schmidhuber, Jürgen: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997)
Jebbara, Soufian, and Philipp Cimiano. 2016. Aspect-based relational sentiment analysis using a stacked neural network architecture. In Proceedings of the Twenty-second European Conference on Artificial Intelligence, 1123–1131. IOS Press
Kim, Yoon. 2014. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882
Li, Xin, Lidong Bing, Piji Li, Wai Lam, and Zhimou Yang. 2018. Aspect term extraction with history attention and selective transformation. arXiv preprint arXiv:1805.00760
Liu, Bing: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781
Mikolov, Tomas, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 3111–3119
Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. 2014. Glove: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543. ACL
Poria, Soujanya, Cambria, Erik, Gelbukh, Alexander: Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems 108, 42–49 (2016)
Qiu, Guang, Liu, Bing, Jiajun, Bu, Chen, Chun: Opinion word expansion and target extraction through double propagation. Computational linguistics 37(1), 9–27 (2011)
Ramshaw, Lance A., and Mitchell P. Marcus. 1999. Text chunking using transformation-based learning. In Natural Language Processing Using Very Large Corpora, 157–176. Berlin: Springer
Schuster, Mike, and Kuldip K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681
Sukhbaatar, Sainbayar, Jason Weston, Rob Fergus, et al. 2015. End-to-end memory networks. In Advances in Neural Information Processing Systems, 2440–2448
Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, 3104–3112
Tang, Duyu, Bing Qin, Xiaocheng Feng, and Ting Liu. 2015. Target-dependent sentiment classification with long short term memory. CoRR, abs/1512.01100
Tang, Duyu, Bing Qin, Xiaocheng Feng, and Ting Liu. Effective LSTMs for target-dependent sentiment classification. [9], 3298–3307
Tang, Duyu, Bing Qin, and Ting Liu. 2016. Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900
Wang, Bo, and Min Liu. 2015. Deep learning for aspect-based sentiment analysis. Stanford University report
Wang, Yequan, Minlie Huang, Li Zhao, et al. 2016. Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 606–615
Wu, Chuhan, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Yongfeng Huang. 2018. A hybrid unsupervised method for aspect term and opinion target extraction. Knowledge-Based Systems 148, 66–73
Xu, Hu, Bing Liu, Lei Shu, and Philip S. Yu. 2018. Double embeddings and cnn-based sequence labeling for aspect extraction. arXiv preprint arXiv:1805.04601
Zhang, Ye, and Byron Wallace. 2015. A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. arXiv preprint arXiv:1510.03820
Zhou, Xinjie, Xiaojun Wan, and Jianguo Xiao. 2015. Representation learning for aspect category detection in online reviews. In Twenty-Ninth AAAI Conference on Artificial Intelligence, 417–424. AAAI Press
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Kumar, A., Sharan, A. (2020). Deep Learning-Based Frameworks for Aspect-Based Sentiment Analysis. In: Agarwal, B., Nayak, R., Mittal, N., Patnaik, S. (eds) Deep Learning-Based Approaches for Sentiment Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1216-2_6
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