I want to do topic modeling on short texts. I did some research on LDA and found that it doesn't go well with short texts. What methods would be better and do they have Python implementations?
You can try Short Text Topic Modelling (refer to this https://www.groundai.com/project/sttm-a-tool-for-short-text-topic-modeling/1) (code available at https://github.com/qiang2100/STTM) . It combine state-of-the-art algorithms and traditional topics modelling for long text which can conveniently be used for short text.
For more specialised libraries, try lda2vec-tf, which combines word vectors with LDA topic vectors. It is branched from the original lda2vec and improved upon and gives better results than the original library.