  This paper is a comparison study in the context of Topic Detection on
COVID-19 data. There are various approaches for Topic Detection, among which
the Clustering approach is selected in this paper. Clustering requires distance
and calculating distance needs embedding. The aim of this research is to
simultaneously study the three factors of embedding methods, distance metrics
and clustering methods and their interaction. A dataset including one-month
tweets collected with COVID-19-related hashtags is used for this study. Five
methods, from earlier to new methods, are selected among the embedding methods:
Word2Vec, fastText, GloVe, BERT and T5. Five clustering methods are
investigated in this paper that are: k-means, DBSCAN, OPTICS, spectral and
Jarvis-Patrick. Euclidian distance and Cosine distance as the most important
distance metrics in this field are also examined. First, more than 7,500 tests
are performed to tune the parameters. Then, all the different combinations of
embedding methods with distance metrics and clustering methods are investigated
by silhouette metric. The number of these combinations is 50 cases. First, the
results of these 50 tests are examined. Then, the rank of each method is taken
into account in all the tests of that method. Finally, the major variables of
the research (embedding methods, distance metrics and clustering methods) are
studied separately. Averaging is performed over the control variables to
neutralize their effect. The experimental results show that T5 strongly
outperforms other embedding methods in terms of silhouette metric. In terms of
distance metrics, cosine distance is weakly better. DBSCAN is also superior to
other methods in terms of clustering methods.
