sentiment_labelled_sentences
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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 ======= Format: ======= sentence \t score \n ======= Details: ======= 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 connotaton, 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