  Short text is a popular avenue of sharing feedback, opinions and reviews on
social media, e-commerce platforms, etc. Many companies need to extract
meaningful information (which may include thematic content as well as semantic
polarity) out of such short texts to understand users' behaviour. However,
obtaining high quality sentiment-associated and human interpretable themes
still remains a challenge for short texts. In this paper we develop ELJST, an
embedding enhanced generative joint sentiment-topic model that can discover
more coherent and diverse topics from short texts. It uses Markov Random Field
Regularizer that can be seen as a generalisation of skip-gram based models.
Further, it can leverage higher-order semantic information appearing in word
embedding, such as self-attention weights in graphical models. Our results show
an average improvement of 10% in topic coherence and 5% in topic
diversification over baselines. Finally, ELJST helps understand users'
behaviour at more granular levels which can be explained. All these can bring
significant values to the service and healthcare industries often dealing with
customers.
