applying word2vec on small text files
<p>I'm totally new to word2vec so please bear it with me. I have a set of text files each containing a set of tweets, between 1000-3000. I have chosen a common keyword (<code>"kw1"</code>) and I want to find semantically relevant terms for <code>"kw1"</code> using word2vec. For example if the keyword is <code>"apple"</code> I would expect to see related terms such as <code>"ipad" "os" "mac"</code>... based on the input file. So this set of related terms for <code>"kw1"</code> would be different for each input file as word2vec would be trained on individual files (eg., 5 input files, run word2vec 5 times on each file). </p>

<p>My goal is to find sets of related terms for each input file given the common keyword (<code>"kw1"</code>), which would be used for some other purposes. </p>

<p>My questions/doubts are:</p>

<ul>
<li>Does it make sense to use word2vec for a task like this? is it technically right to use considering the small size of an input file?</li>
</ul>

<p>I have downloaded the code from code.google.com: <a href="https://code.google.com/p/word2vec/" rel="nofollow">https://code.google.com/p/word2vec/</a> and have just given it a dry run as follows:</p>

<pre><code> time ./word2vec -train $file -output vectors.bin -cbow 1 -size 200 -window 10 -negative 25 -hs 1 -sample 1e-3 -threads 12 -binary 1 -iter 50

./distance vectors.bin 
</code></pre>

<ul>
<li><p>From my results I saw I'm getting many noisy terms (stopwords) when I'm using the <code>'distance'</code> tool to get related terms to <code>"kw1"</code>. So I did remove stopwords and other noisy terms such as user mentions. But I haven't seen anywhere that word2vec requires cleaned input data?</p></li>
<li><p>How do you choose right parameters? I see the results (from running the <code>distance</code> tool) varies greatly when I change parameters such as <code>'-window'</code>, <code>'-iter'</code>. Which technique should I use to find the correct values for the parameters. (manual trial and error is not possible for me as I'll be scaling up the dataset). </p></li>
</ul>