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Employing hierarchical Bayesian networks in simple and complex emotion topic analysis

机译：在简单和复杂的情感主题分析中使用分层贝叶斯网络

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摘要

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.
机译：传统的情感模型在标记文档中的单个情感时，通常会忽略大多数文档传达复杂的人类情感这一事实。在本文中，我们将情感分析与主题模型结合在一起，以发现文档中的复杂情感以及情感强度，并研究文档情感如何随主题而变化。贝叶斯网络被用来产生潜在的话题变量和情感变量。平均而言，我们基于单一情感分类的模型优于传统的受监督的机器学习模型，例如SVM和朴素贝叶斯。复杂情绪分类的另一个模型也取得了可喜的结果。我们在实验中彻底分析了词汇质量和主题数量对情绪和强度预测的影响。发现诸如Friend和Job之类的主题分布对文档的情绪敏感，在本文中我们将其称为情绪主题变化。这揭示了话题与情感之间更深层次的关系。

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