<p>You really only would need to make a list of 100 or so, each, of negative and positive adjectives.</p>

<p>See:<br/>
<a href="http://na2english.wikispaces.com/file/view/ADJECTIVES%20TO%20DESCRIBE%20FILMS.pdf/400672720/ADJECTIVES%20TO%20DESCRIBE%20FILMS.pdf" rel="nofollow">http://na2english.wikispaces.com/file/view/ADJECTIVES%20TO%20DESCRIBE%20FILMS.pdf/400672720/ADJECTIVES%20TO%20DESCRIBE%20FILMS.pdf</a><br/><br/>
<a href="http://arxiv.org/ftp/arxiv/papers/1011/1011.4623.pdf" rel="nofollow">http://arxiv.org/ftp/arxiv/papers/1011/1011.4623.pdf</a><br/><br/></p>

<p>Obviously cite them if you use them, but language is free, so you can use those for your work.</p>

<p>Probably more important than the size of the database you construct will be picking words that target your specific application for increased efficacy.</p>

<p>Are you aiming this project at a specific commercial use or as a more generalized research effort?</p>
Whats the minimum training set size needed for sentiment classification task on short texts
<p>I'm trying to train an LSTM model for the task of sentiment classification on short texts such as products reviews and tweets. </p>

<p>I'm looking for a training set that labels positive/negative/neutral, is there such thing (free for research) out there that is really based on human tags and not on starts or emoticons? Iv'e found only small training sets which led me to poor results. Iv'e tried to increase the size of my network and stacked layers but no improvement. </p>

<p>Whats the minimum size for such a training set in order to start getting reasonable results (F1 > 0.8).</p>