language: | |
- en | |
https://archive.ics.uci.edu/dataset/331/sentiment+labelled+sentences | |
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 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 | |