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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