Recently I have played with the pretrained GLOVE word embedding model for Twitter
I notice that common stopwords are existing in the model. That is, there is no stopword filtering before the training of the model.
I wonder if stopword filtering would improve performance in terms of:
- higher correlation (or cos-sim) between semantically similar words
- less noisy sum for aggregation of set of words, because I've heard the main problem of aggregation of word embedding is poor weighing on a significant portion of noisy words in the set.
Or does filtering stopwords give problems that I am not seeing?