I am working on sentiment analysis. I am using Word2Vec method. I don't understand the output from this code line.
dum_out = Word2Vec(size = 200,min_count = 10, window = 5 )
It gives a vector of 200 dimensions for each word(let us take a word "break"). So
dum_out['break'] gives me a vector of 200 dimensions. I I understand that these numbers in the vector are probabilities of how close 'break' is to 'other words' . But my question is what are these 'other words' ? With what context are these figures calculated ? How does the 'dimensions' parameter affect the model ?
(You can consider any vocabulary size if needed and any other parameters accordingly)