  Topic modeling is admittedly a convenient way to monitor markets trend.
Conventionally, Latent Dirichlet Allocation, LDA, is considered a must-do model
to gain this type of information. By given the merit of deducing keyword with
token conditional probability in LDA, we can know the most possible or
essential topic. However, the results are not intuitive because the given
topics cannot wholly fit human knowledge. LDA offers the first possible
relevant keywords, which also brings out another problem of whether the
connection is reliable based on the statistic possibility. It is also hard to
decide the topic number manually in advance. As the booming trend of using
fuzzy membership to cluster and using transformers to embed words, this work
presents the fuzzy topic modeling based on soft clustering and document
embedding from state-of-the-art transformer-based model. In our practical
application in a press release monitoring, the fuzzy topic modeling gives a
more natural result than the traditional output from LDA.
