Abstract
Most online news websites have enabled users to annotate their sentiments while reading the news. Different from traditional users’ feedbacks such as reviews or ratings, those annotations are more intuitive to express the sentiment of the users. Topic model is proved more effective to analyze the text information, however, most existing topic models focus on either extracting static topic sentiment or tracking topics over time but ignoring sentiment analysis. In the paper, we propose a joint topic-sentiment over time model (JTSoT) to detect the topic-sentiment shift and track the topic evolution over time. The critical challenge is how to balance the relationship among the topic, sentiment and time. The topic is represented as a Beta distribution over time and a Dirichlet distribution with respect to the sentiment. We evaluate our method on the real-world news dataset. The experimental results show that we have achieved high correlation between the topic and sentiment, better interpretable topic evolution, and higher document sentiment classification result and perplexity.
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This work was supported by Natural Science Foundation of China (No. 61170192).
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Hu, Y., Xu, X., Li, L. (2016). Analyzing Topic-Sentiment and Topic Evolution over Time from Social Media. In: Lehner, F., Fteimi, N. (eds) Knowledge Science, Engineering and Management. KSEM 2016. Lecture Notes in Computer Science(), vol 9983. Springer, Cham. https://doi.org/10.1007/978-3-319-47650-6_8
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DOI: https://doi.org/10.1007/978-3-319-47650-6_8
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