AlexSham/amazon_cells_filtered

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

原始信息汇总

数据集概述

数据集来源

• 该数据集是为论文 From Group to Individual Labels using Deep Features, Kotzias et. al,. KDD 2015 创建的。

数据内容

• 包含从产品、电影和餐厅评论中提取的带有情感标签的句子。
• 情感标签分为正面（1）和负面（0）。

数据格式

• 数据格式为：sentence score

数据详情

• 句子来源于以下三个网站：
  • imdb.com
  • amazon.com
  • yelp.com
• 每个网站各有500条正面和500条负面句子，这些句子是从更大的评论数据集中随机选取的。
• 选取的句子旨在具有明确的正面或负面含义，避免包含中性句子。

引用信息

• 若使用此数据集，请引用上述论文。