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Latent Discriminative Models For Social Emotion Detection With Emotional Dependency

Xiaojun Quan, Qifan Wang, Ying Zhang, Luo Si, Liu Wenyin

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Sentiment analysis of such opinionated online texts as reviews and comments has received increasingly close attention, yet most of the work is intended to deal with the detection of authors’ emotion. In contrast, this article presents our study of the social emotion detection problem, the objective of which is to identify the evoked emotions of readers by online documents such as news articles. A novel Latent Discriminative Model (LDM) is proposed for this task. LDM works by introducing intermediate hidden variables to model the latent structure of input text corpora. To achieve this, it defines a joint distribution over emotions and latent variables, conditioned on the observed text documents. Moreover, we assume that social emotions are not independent but correlated with one another, and the dependency of them is capable of providing additional guidance to LDM in the training process. The inclusion of this emotional dependency into LDM gives rise to a new Emotional Dependency-based LDM (eLDM). We evaluate the proposed models through a series of empirical evaluations on two real-world corpora of news articles. Experimental results verify the effectiveness of LDM and eLDM in social emotion detection.