Unsupervised Topic Discovery in User Comments

TL;DR

This paper presents an unsupervised deep learning approach for automatically discovering semantically coherent topics in user comments, aiding stakeholders in extracting valuable insights without manual effort.

Contribution

It introduces a novel deep NLP-based method for unsupervised topic discovery in user comments that requires no parameter tuning and demonstrates high cluster cohesion and meaningfulness.

Findings

High inter-coder agreement (up to 98%) in evaluation

Effective thematic analysis on telecommunication tweets

Robustness of approach without parameter configuration

Abstract

On social media platforms like Twitter, users regularly share their opinions and comments with software vendors and service providers. Popular software products might get thousands of user comments per day. Research has shown that such comments contain valuable information for stakeholders, such as feature ideas, problem reports, or support inquiries. However, it is hard to manually manage and grasp a large amount of user comments, which can be redundant and of a different quality. Consequently, researchers suggested automated approaches to extract valuable comments, e.g., through problem report classifiers. However, these approaches do not aggregate semantically similar comments into specific aspects to provide insights like how often users reported a certain problem. We introduce an approach for automatically discovering topics composed of semantically similar user comments based on deep bidirectional natural language processing algorithms. Stakeholders can use our approach without the need to configure critical parameters like the number of clusters. We present our approach and report on a rigorous multiple-step empirical evaluation to assess how cohesive and meaningful the resulting clusters are. Each evaluation step was peer-coded and resulted in inter-coder agreements of up to 98%, giving us high confidence in the approach. We also report a thematic analysis on the topics discovered from tweets in the telecommunication domain.