This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015
Please cite the paper if you want to use it :)

It contains sentences labelled with positive or negative sentiment, extracted from reviews of products, movies, and restaurants

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Format:
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sentence \t score \n


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Details:
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Score is either 1 (for positive) or 0 (for negative)	
The sentences come from three different websites/fields:

imdb.com
amazon.com
yelp.com

For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews. 
We attempted to select sentences that have a clearly positive or negative connotation, the goal was for no neutral sentences to be selected.



For the full datasets look:

imdb: Maas et. al., 2011 'Learning word vectors for sentiment analysis'
amazon: McAuley et. al., 2013 'Hidden factors and hidden topics: Understanding rating dimensions with review text'
yelp: Yelp dataset challenge http://www.yelp.com/dataset_challenge
