Abstract
Opinion mining of social networking sites like Facebook and Twitter plays an important role in exploring valuable online user-generated contents. In contrast to sentence-level sentiment classification, the aspect-based analysis which can infer polarities towards various aspects in one sentence could obtain more in-depth insight. However, in traditional machine learning approaches, training such a fine-grained model often needs certain manual feature engineering. In this article, we proposed a deep learning model for aspect-level sentiment analysis and applied it to nuclear energy related tweets for understanding public opinions towards nuclear energy. We also built a new dataset for this task and the evaluation results showed that our attentive neural network could obtain insightful inference in rather complex expression forms and achieve state-of-the-art performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: what 140 characters reveal about political sentiment. Icwsm 10(1), 178–185 (2010)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)
Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27–35 (2014)
Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. arXiv preprint arXiv:1308.6242 (2013)
Thet, T.T., Na, J.C., Khoo, C.S.: Aspect-based sentiment analysis of movie reviews on discussion boards. J. Inf. Sci. 36(6), 823–848 (2010)
Dzmitry, B., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Rocktäschel, T., Grefenstette, E., Hermann, K.M., Kočiský, T., Blunsom, P.: Reasoning about entailment with neural attention. arXiv preprint arXiv:1509.06664 (2015)
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)
Sukhbaatar, S., Weston, J., Fergus, R.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, pp. 2440–2448 (2015)
Wang, Y., Huang, M., Zhao, L.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016)
Kim, D.S., Kim, J.W.: Public opinion sensing and trend analysis on social media: a study on nuclear power on Twitter. Int. J. Multimed. Ubiquitous Eng. 9(11), 373–384 (2014)
Kumar, A., et al.: Ask me anything: dynamic memory networks for natural language processing. In: International Conference on Machine Learning, pp. 1378–1387 (2016)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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
Liu, Z., Na, JC. (2018). Aspect-Based Sentiment Analysis of Nuclear Energy Tweets with Attentive Deep Neural Network. In: Dobreva, M., Hinze, A., Žumer, M. (eds) Maturity and Innovation in Digital Libraries. ICADL 2018. Lecture Notes in Computer Science(), vol 11279. Springer, Cham. https://doi.org/10.1007/978-3-030-04257-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-04257-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04256-1
Online ISBN: 978-3-030-04257-8
eBook Packages: Computer ScienceComputer Science (R0)