Using it for assessment.

# Dataset for Multi Domain （Including Kitchen, Books, DVDs, and Electronics）

[Multi-Domain Sentiment Dataset](https://www.cs.jhu.edu/~mdredze/datasets/sentiment/index2.html) by John Blitzer, Mark Dredze, Fernando Pereira.

### Description：
The Multi-Domain Sentiment Dataset contains product reviews taken from Amazon.com from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. This page contains some descriptions about the data. If you have questions, please email me directly (email found here).

A few notes regarding the data.

1) There are 4 directories corresponding to each of the four domains. Each directory contains 3 files called positive.review, negative.review and unlabeled.review. (The books directory doesn't contain the unlabeled but the link is below.) While the positive and negative files contain positive and negative reviews, these aren't necessarily the splits we used in the experiments. We randomly drew from the three files ignoring the file names.

2) Each file contains a pseudo XML scheme for encoding the reviews. Most of the fields are self explanatory. The reviews have a unique ID field that isn't very unique. If it has two unique id fields, ignore the one containing only a number.

### Link to download the data:

Multi-Domain Sentiment Dataset (30 MB) [domain_sentiment_data.tar.gz](https://www.cs.jhu.edu/~mdredze/datasets/sentiment/domain_sentiment_data.tar.gz)

Books unlabeled data (2 MB) [book.unlabeled.gz](https://www.cs.jhu.edu/~mdredze/datasets/sentiment/book.unlabeled.gz)

region:us
{}
TAGS
#region-us 

Using it for assessment.

# Dataset for Multi Domain （Including Kitchen, Books, DVDs, and Electronics）

Multi-Domain Sentiment Dataset by John Blitzer, Mark Dredze, Fernando Pereira.

### Description：
The Multi-Domain Sentiment Dataset contains product reviews taken from URL from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. This page contains some descriptions about the data. If you have questions, please email me directly (email found here).

A few notes regarding the data.

1) There are 4 directories corresponding to each of the four domains. Each directory contains 3 files called URL, URL and URL. (The books directory doesn't contain the unlabeled but the link is below.) While the positive and negative files contain positive and negative reviews, these aren't necessarily the splits we used in the experiments. We randomly drew from the three files ignoring the file names.

2) Each file contains a pseudo XML scheme for encoding the reviews. Most of the fields are self explanatory. The reviews have a unique ID field that isn't very unique. If it has two unique id fields, ignore the one containing only a number.

### Link to download the data:

Multi-Domain Sentiment Dataset (30 MB) domain_sentiment_data.URL

Books unlabeled data (2 MB) URL

# Dataset for Multi Domain （Including Kitchen, Books, DVDs, and Electronics）

Multi-Domain Sentiment Dataset by John Blitzer, Mark Dredze, Fernando Pereira. ### Description：
The Multi-Domain Sentiment Dataset contains product reviews taken from URL from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. This page contains some descriptions about the data. If you have questions, please email me directly (email found here).

A few notes regarding the data.

1) There are 4 directories corresponding to each of the four domains. Each directory contains 3 files called URL, URL and URL. (The books directory doesn't contain the unlabeled but the link is below.) While the positive and negative files contain positive and negative reviews, these aren't necessarily the splits we used in the experiments. We randomly drew from the three files ignoring the file names.

2) Each file contains a pseudo XML scheme for encoding the reviews. Most of the fields are self explanatory. The reviews have a unique ID field that isn't very unique. If it has two unique id fields, ignore the one containing only a number. ### Link to download the data:

Multi-Domain Sentiment Dataset (30 MB) domain_sentiment_data.URL

Books unlabeled data (2 MB) URL
TAGS
#region-us 
 # Dataset for Multi Domain （Including Kitchen, Books, DVDs, and Electronics）

Multi-Domain Sentiment Dataset by John Blitzer, Mark Dredze, Fernando Pereira. ### Description：
The Multi-Domain Sentiment Dataset contains product reviews taken from URL from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. This page contains some descriptions about the data. If you have questions, please email me directly (email found here).

A few notes regarding the data.

1) There are 4 directories corresponding to each of the four domains. Each directory contains 3 files called URL, URL and URL. (The books directory doesn't contain the unlabeled but the link is below.) While the positive and negative files contain positive and negative reviews, these aren't necessarily the splits we used in the experiments. We randomly drew from the three files ignoring the file names.

2) Each file contains a pseudo XML scheme for encoding the reviews. Most of the fields are self explanatory. The reviews have a unique ID field that isn't very unique. If it has two unique id fields, ignore the one containing only a number. ### Link to download the data:

Multi-Domain Sentiment Dataset (30 MB) domain_sentiment_data.URL

Books unlabeled data (2 MB) URL
passage: TAGS
#region-us 
# Dataset for Multi Domain （Including Kitchen, Books, DVDs, and Electronics）

Multi-Domain Sentiment Dataset by John Blitzer, Mark Dredze, Fernando Pereira.### Description：
The Multi-Domain Sentiment Dataset contains product reviews taken from URL from 4 product types (domains): Kitchen, Books, DVDs, and Electronics. Each domain has several thousand reviews, but the exact number varies by domain. Reviews contain star ratings (1 to 5 stars) that can be converted into binary labels if needed. This page contains some descriptions about the data. If you have questions, please email me directly (email found here).

A few notes regarding the data.

1) There are 4 directories corresponding to each of the four domains. Each directory contains 3 files called URL, URL and URL. (The books directory doesn't contain the unlabeled but the link is below.) While the positive and negative files contain positive and negative reviews, these aren't necessarily the splits we used in the experiments. We randomly drew from the three files ignoring the file names.

2) Each file contains a pseudo XML scheme for encoding the reviews. Most of the fields are self explanatory. The reviews have a unique ID field that isn't very unique. If it has two unique id fields, ignore the one containing only a number.### Link to download the data:

Multi-Domain Sentiment Dataset (30 MB) domain_sentiment_data.URL

Books unlabeled data (2 MB) URL