# Deep Representation Learning for Clustering of Health Tweets

arXiv: https://arxiv.org/abs/1901.00439 · HTML: https://mlsanity.com/paper/1901.00439/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Clustering Vietnamese Conversations From Facebook Page To Build Training Dataset For Chatbot](https://mlsanity.com/paper/2112.15338/) (2021)
- [Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions](https://mlsanity.com/paper/2604.06214/) (2026)

## Abstract (arXiv)

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.