  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.
