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Dataset: yelp_polarity 🏷
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from datasets import load_dataset dataset = load_dataset("yelp_polarity")


Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to The Yelp reviews polarity dataset is constructed by Xiang Zhang ( from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015). DESCRIPTION The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2. The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 and 2) and review text. The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".


  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1509.01626},
  primaryClass = {cs},
  title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
  abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},
  journal = {arXiv:1509.01626 [cs]},
  author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
  month = sep,
  year = {2015},

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