Deep Representation Learning for Clustering of Health Tweets
This work addresses the need for accurate topic clustering of health tweets to extract insights, but it is incremental as it applies an existing deep learning method to a specific domain.
The authors tackled the problem of clustering health-related tweets by proposing deep convolutional autoencoders to learn compact representations, which significantly outperformed conventional methods like bag-of-words and LDA across different numbers of clusters.
Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tweets, further to be employed in clustering. We compare our method to several conventional tweet representation methods including bag-of-words, term frequency-inverse document frequency, Latent Dirichlet Allocation and Non-negative Matrix Factorization with 3 different clustering algorithms. Our results show that the clustering performance using proposed representation learning scheme significantly outperforms that of conventional methods for all experiments of different number of clusters. In addition, we propose a constraint on the learned representations during the neural network training 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 representations of health-related tweets.