Hybrid neural networks for social emotion detection over short text

Short text is prevalent on the Web, but it brings challenges to content analysis methods for the lack of contextual information. Biterm topic model (BTM) is a variant of latent Dirichlet allocation, which effectively infers the latent topic distribution of short text by modeling the generation of biterms in the whole corpus. However, it needs fine-tuning from labels to reduce noise when applied to supervised learning. Motivated by the transfer learning approach, we propose the hybrid neural networks based on BTM and conventional neural networks, which first make the hidden layer of neural networks approximate the inference of BTM. Following this initial pre-training phase, we then use the simple back-propagation algorithm to fine-tune the topic distribution learned from BTM, so as to improve the performance of supervised learning. Our experiment on two diverse collections of short text validates the effectiveness of the proposed hybrid neural networks for social emotion detection.