  This work combines algorithms based on word embeddings, dimensionality
reduction, and clustering. The objective is to obtain topics from a set of
unclassified texts. The algorithm to obtain the word embeddings is the BERT
model, a neural network architecture widely used in NLP tasks. Due to the high
dimensionality, a dimensionality reduction technique called UMAP is used. This
method manages to reduce the dimensions while preserving part of the local and
global information of the original data. K-Means is used as the clustering
algorithm to obtain the topics. Then, the topics are evaluated using the TF-IDF
statistics, Topic Diversity, and Topic Coherence to get the meaning of the
words on the clusters. The results of the process show good values, so the
topic modeling of this process is a viable option for classifying or clustering
texts without labels.
