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.
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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
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DOI: https://doi.org/10.1007/978-3-030-04257-8_9
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