Clustering Tweet Data using DBSCAN Algorithm
Two things: eps and quantitative representation of text.
You see that there is only for eps=0.15 a lot of clusters. But for others a lot less. This is hyper parameter that needs to be optimised (and min_samples)
And the other thing thats more important is what you use quantitative representation of text. You said Bag of Words, TFIDF, Spacy Vectors and also, Word2Vec, but did you tune them? DId you tree embeddings etc etc. There is a lot of improvement here, and when its good dbscan will function a lot better.