Abstract
In this paper, we describe a deep neural network architecture for domain-aware sentiment classification task with the purpose of the sentiment classification of product reviews in different domains and evaluating nine pre-trained embeddings provided by the semantic sentiment classification challenge at the 15th Extended Semantic Web Conference. The proposed approach combines the domain and the sequence of word embeddings of the summary or text of each review for Gated Recurrent Units (GRUs) to produce the corresponding sequence of embeddings by being aware of the domain and previous words. Afterwards, it extracts local features using Convolutional Neural Networks (CNNs) from the output of the GRU layer. The two sets of local features extracted from the domain-aware summary and text of a review are concatenated into a single vector, and are used for classifying the sentiment of a review. Our approach obtained 0.9643 F1-score on the test set and achieved the 1st place in the first task of the Semantic Sentiment Analysis Challenge at the 15th Extended Semantic Web Conference.
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
Atzeni, Mattia, Dridi, Amna, Reforgiato Recupero, Diego: Fine-grained sentiment analysis on financial microblogs and news headlines. In: Dragoni, Mauro, Solanki, Monika, Blomqvist, Eva (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 124–128. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69146-6_11
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Cliche, M.: BB\_twtr at SemEval-2017 Task 4: twitter Sentiment Analysis with CNNs and LSTMs. CoRR abs/1704.0 (2017), arXiv:1704.06125
Cortis, K., et al.: Semeval-2017 task 5: fine-grained sentiment analysis on financial microblogs and news. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 519–535 (2017)
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)
Federici, Marco, Dragoni, Mauro: A knowledge-based approach for aspect-based opinion mining. In: Sack, Harald, Dietze, Stefan, Tordai, Anna, Lange, Christoph (eds.) SemWebEval 2016. CCIS, vol. 641, pp. 141–152. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46565-4_11
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Iguider, Walid, Reforgiato Recupero, Diego: Language independent sentiment analysis of the shukran social network using apache spark. In: Dragoni, Mauro, Solanki, Monika, Blomqvist, Eva (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 129–132. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69146-6_12
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: The 52nd Annual Meeting of the Association for Computational Linguistics (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Conference on Empirical Methods on Natural Language Processing (2014)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems, pp. 396–404 (1990)
Petrucci, Giulio, Dragoni, Mauro: The IRMUDOSA system at ESWC-2017 challenge on semantic sentiment analysis. In: Dragoni, Mauro, Solanki, Monika, Blomqvist, Eva (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 148–165. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69146-6_14
Piao, G., Breslin, J.G.: Financial aspect and sentiment predictions with deep neural networks: an ensemble approach. In: Financial Opinion Mining and Question Answering Workshop at The Web Conference (WWW). ACM (2018)
Reforgiato Recupero, Diego, Cambria, Erik, Di Rosa, Emanuele: Semantic sentiment analysis challenge at ESWC2017. In: Dragoni, Mauro, Solanki, Monika, Blomqvist, Eva (eds.) SemWebEval 2017. CCIS, vol. 769, pp. 109–123. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69146-6_10
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives. CoRR abs/1707.0 (2017), arXiv:1707.07435
Zhang, Y., Wallace, B.C.: A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. CoRR abs/1510.0 (2015), arXiv:1510.03820
Acknowledgments
This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 (Insight Centre for Data Analytics).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Piao, G., Breslin, J.G. (2018). Domain-Aware Sentiment Classification with GRUs and CNNs. In: Buscaldi, D., Gangemi, A., Reforgiato Recupero, D. (eds) Semantic Web Challenges. SemWebEval 2018. Communications in Computer and Information Science, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-00072-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-00072-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00071-4
Online ISBN: 978-3-030-00072-1
eBook Packages: Computer ScienceComputer Science (R0)