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![]() And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. ![]() Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things (IoT).
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