Employing hierarchical Bayesian networks in simple and complex emotion topic analysis

Traditional emotion models, when tagging single emotions in documents, often ignore the fact that most documents convey complex human emotions. In this paper, we join emotion analysis with topic models to find complex emotions in documents, as well as the intensity of the emotions, and study how the document emotions vary with topics. Hierarchical Bayesian networks are employed to generate the latent topic variables and emotion variables. On average, our model on single emotion classification outperforms the traditional supervised machine learning models such as SVM and Naive Bayes. The other model on the complex emotion classification also achieves promising results. We thoroughly analyze the impact of vocabulary quality and topic quantity to emotion and intensity prediction in our experiments. The distribution of topics such as Friend and Job are found to be sensitive to the documents’ emotions, which we call emotion topic variation in this paper. This reveals the deeper relationship between topics and emotions.

► We provide 2 models for analyzing emotions (intensities) and topics from documents.
► We use Gibbs sampling for inference.
► Results of simple emotion classification are better than traditional approaches.
► Results of complex emotions and emotion intensities classification are promising.
► Emotion topic variation shows emotional explanation for the generated topics.