Optimized Tracking of Topic Evolution

TL;DR

This paper enhances topic evolution modeling by introducing filtering and network analysis techniques to reduce noise and improve interpretability, validated through qualitative and quantitative evaluations.

Contribution

It proposes novel filtering and ranking methods to improve the quality and clarity of topic modeling results in evolving topic analysis.

Findings

Improved topic coherence scores in quantitative evaluation.

Reduced noise and unimportant topics in results.

Faster execution times with the proposed methods.

Abstract

Topic evolution modeling has been researched for a long time and has gained considerable interest. A state-of-the-art method has been recently using word modeling algorithms in combination with community detection mechanisms to achieve better results in a more effective way. We analyse results of this approach and discuss the two major challenges that this approach still faces. Although the topics that have resulted from the recent algorithm are good in general, they are very noisy due to many topics that are very unimportant because of their size, words, or ambiguity. Additionally, the number of words defining each topic is too large, making it difficult to analyse them in their unsorted state. In this paper, we propose approaches to tackle these challenges by adding topic filtering and network analysis metrics to define the importance of a topic. We test different combinations of these metrics to see which combination yields the best results. Furthermore, we add word filtering and ranking to each topic to identify the words with the highest novelty automatically. We evaluate our enhancement methods in two ways: human qualitative evaluation and automatic quantitative evaluation. Moreover, we created two case studies to test the quality of the clusters and words. In the quantitative evaluation, we use the pairwise mutual information score to test the coherency of topics. The quantitative evaluation also includes an analysis of execution times for each part of the program. The results of the experimental evaluations show that the two evaluation methods agree on the positive feasibility of the algorithm. We then show possible extensions in the form of usability and future improvements to the algorithm.