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Twitter has been a prominent social media platform for miningpopulation-level health data and accurate clustering of health-related tweetsinto topics is important for extracting relevant health insights. In this work,we propose deep convolutional autoencoders for learning compact representationsof health-related tweets, further to be employed in clustering. We compare ourmethod to several conventional tweet representation methods includingbag-of-words, term frequency-inverse document frequency, Latent DirichletAllocation and Non-negative Matrix Factorization with 3 different clusteringalgorithms. Our results show that the clustering performance using proposedrepresentation learning scheme significantly outperforms that of conventionalmethods for all experiments of different number of clusters. In addition, wepropose a constraint on the learned representations during the neural networktraining in order to further enhance the clustering performance. All in all,this study introduces utilization of deep neural network-based architectures,i.e., deep convolutional autoencoders, for learning informative representationsof health-related tweets.

Deep Representation Learning for Clustering of Health Tweets