Large quantities of textual posts are increasingly generated in course discussion forums, and the accumulation of these data greatly increases the cognitive loads on online participants. It is imperative for them to automatically identify the potential semantic information derived from these textual discourse interactions. Moreover, existing topic models can discover the latent topics or sentimental polarities from textual data, but these models typically ignore the interactive ways of discussing topics, thus making it difficult to further construct topics’ semantic space from the perspective of document generation. To solve this issue, we proposed a joint sentiment and behaviour topic model called SBTM, which was an unsupervised approach for automatic analysis of learners’ discussed posts. The results demonstrated that SBTM was quantitatively effective on both model generalisation and topic exploration, and rich topic content was qualitatively characterised. Furthermore, the model can be potentially employed in some practical applications, such as information summarisation and behaviour-oriented personalised recommendation.
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