I am trying to do sentiment analysis. In order to convert the words to word vectors I am using word2vec model from gensim package. Suppose I have all the sentences in a list named 'sentences' and I am passing these sentences to word2vec as follows :
model = word2vec.Word2Vec(sentences, workers=4 , min_count=40, size=300, window=5, sample=1e-3)
Since I am noob to word vectors I have two doubts.
1- Setting the number of features to 300 defines the features of a word vector. But what these features signify? If each word in this model is represented by a 1x300 numpy array, then what do these 300 features signify for that word?
2- What does down sampling as represented by 'sample' parameter in the above model do in actual?
Thanks in advance.