I have a database containing about 3 million texts (tweets). I put clean texts (removing stop words, tags...) in a list of lists of tokens called sentences (so it contains a list of tokens for each text).
After these steps, if I write
model = Word2Vec(sentences, min_count=1)
I obtain a vocabulary of about 400,000 words.
I have also a list of words (belonging to the same topic, in this case: economics) called terms. I found that 7% of the texts contain at least one of these words (so we can say that 7% of total tweets talk about economics).
My goal is to expand the list terms in order to retrieve more texts belonging to the economic topic.
Then I use
results = model.most_similar(terms, topn=5000)
to find, within the list of lists of tokens sentences, the words most similar to those contained in terms.
Finally if I create the data frame
df = pd.DataFrame(results, columns=['key', 'similarity'])
I get something like that:
key similarity
word1 0.795432
word2 0.787954
word3 0.778942
... ...
Now I think I have two possibilities to define the expanded glossary:
How should I proceed in a case like this?
There's no general answer for what the cutoff should be, or how much you should use your own manual judgement versus cruder (but fast/automatic) processes. Those are inherently decisions which will be heavily influenced by your data, model quality, & goals – so you have to try different approaches & see what works there.
If you had a goal for what percentage of the original corpus you want to take – say, 14% instead of 7% – you could go as deeply into the ranked candidate list of 'similar words' as necessary to hit that 14% target.
Note that when you retrieve model.most_similar(terms), you are asking the model to 1st average all words in terms together, then return words close to that one average point. To the extent your seed set of terms is tightly around the idea of economics, that might find words close to that generic average idea – but might not find other interesting words, such as close sysnonyms of your seed words that you just hadn't thought of. For that, you might want to get not 5000 neighbors for one generic average point, but (say) 3 neighbors for every individual term. To the extent the 'shape' of the topic isn't a perfect sphere around someplace in the word-vector-space, but rather some lumpy complex volume, that might better reflect your intent.
Instead of using your judgement of the candidate words standing alone to decide whether a word is economics-related, you could instead look at the texts that a word uniquely brings in. That is, for new word X, look at the N texts that contain that word. How many, when applying your full judgement to their full text, deserve to be in your 'economics' subset? Only if it's above some threshold T would you want to move X into your glossary.
But such an exercise may just highlight: using a simple glossary – "for any of these hand-picked N words, every text mentioning at least 1 word is in" – is a fairly crude way of assessing a text's topic. There are other ways to approach the goal of "pick a relevant subset" in an automated way.
For example, you could view your task as that of training a text binary classifier to classify texts as 'economics' or 'not-economics'.
In such a case, you'd start with some training data - a set of example documents that are already labeled 'economics' or 'not-economics', perhaps via individual manual review, or perhaps via some crude bootstrapping (like labeling all texts with some set of glossary words as 'economics', & all others 'not-economics'). Then you'd draw from the full range of potential text-preprocessing, text-feature-extracton, & classification options to train & evaluate classifiers that make that judgement for you. Then you'd evaluate/tune those – a process wich might also improve your training data, as you add new definitively 'economics' or 'not-economics' texts – & eventually settle on one that works well.
Alternatively, you could use some other richer topic-modeling methods (LDA, word2vec-derived Doc2Vec, deeper neural models etc) for modeling the whole dataset, then from some seed-set of definite-'economics' texts, expand outward from them – finding nearest-examples to known-good documents, either auto-including them or hand-reviewing them.
Separately: min_count=1 is almost always a mistake in word2vec & related algorihtms, which do better if you discard words so rare they lack the variety of multiple usage examples the algorithm needs to generate good word-vectors.