Abstract
Twitter has gradually become a valuable source of people’s opinions and sentiments. Although tremendous progress has been made in sentiment analysis, mainstream methods hardly leverage user information. Besides, most methods strongly rely on sentiment lexicons in tweets, thus ignoring other non-sentiment words that imply rich topic information. This paper aims to predict individuals’ sentiment towards potential topics on a two-point scale: positive or negative. The analysis is conducted based on their past tweets for the precise topic recommendation. We propose a hierarchical model of individuals’ tweets (HMIT) to explore the relationship between individual sentiments and different topics. HMIT extracts token representations from fine-tuned Bidirectional Encoder Representations from Transformer (BERT). Then it incorporates topic information in context-aware token representations through a topic-level attention mechanism. The Convolutional Neural Network (CNN) serves as a final binary classifier. Unlike conventional sentiment classification in the Twitter task, HMIT extracts topic phrases through Single-Pass and feeds tweets without sentiment words into the whole model. We build six user models from one benchmark and our collected datasets. Experimental results demonstrate the superior performance of the proposed method against multiple baselines on both classification and quantification tasks.
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Acknowledgment
This research work has been funded by the National Natural Science Foundation of China (Grant No. 61772337), the National Key Research and Development Program of China NO. 2016QY03D0604.
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Ji, Q., Dai, Y., Ma, Y., Liu, G., Zhang, Q., Lin, X. (2020). Hierarchical Sentiment Estimation Model for Potential Topics of Individual Tweets. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_75
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DOI: https://doi.org/10.1007/978-3-030-63820-7_75
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