  Customers' reviews and comments are important for businesses to understand
users' sentiment about the products and services. However, this data needs to
be analyzed to assess the sentiment associated with topics/aspects to provide
efficient customer assistance. LDA and LSA fail to capture the semantic
relationship and are not specific to any domain. In this study, we evaluate
BERTopic, a novel method that generates topics using sentence embeddings on
Consumer Financial Protection Bureau (CFPB) data. Our work shows that BERTopic
is flexible and yet provides meaningful and diverse topics compared to LDA and
LSA. Furthermore, domain-specific pre-trained embeddings (FinBERT) yield even
better topics. We evaluated the topics on coherence score (c_v) and UMass.
