sentiment140

• Description:

Sentiment140 allows you to discover the sentiment of a brand, product, or topic on Twitter.

The data is a CSV with emoticons removed. Data file format has 6 fields:

1. the polarity of the tweet (0 = negative, 2 = neutral, 4 = positive)
2. the id of the tweet (2087)
3. the date of the tweet (Sat May 16 23:58:44 UTC 2009)
4. the query (lyx). If there is no query, then this value is NO_QUERY.
5. the user that tweeted (robotickilldozr)
6. the text of the tweet (Lyx is cool)

For more information, refer to the paper Twitter Sentiment Classification with Distant Supervision at https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf

• Additional Documentation: Explore on Papers With Code

• Homepage: http://help.sentiment140.com/home

• Source code: tfds.datasets.sentiment140.Builder

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 77.59 MiB

• Dataset size: 305.13 MiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'test'	498
'train'	1,600,000

• Feature structure:

FeaturesDict({
    'date': Text(shape=(), dtype=string),
    'polarity': int32,
    'query': Text(shape=(), dtype=string),
    'text': Text(shape=(), dtype=string),
    'user': Text(shape=(), dtype=string),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
date	Text		string
polarity	Tensor		int32
query	Text		string
text	Text		string
user	Text		string

• Supervised keys (See as_supervised doc): ('text', 'polarity')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

@ONLINE {Sentiment140,
    author = "Go, Alec and Bhayani, Richa and Huang, Lei",
    title  = "Twitter Sentiment Classification using Distant Supervision",
    year   = "2009",
    url    = "http://help.sentiment140.com/home"
}