
# SARC_Sarcasm

## Dataset Description

- **Paper:** [A Large Self-Annotated Corpus for Sarcasm](http://www.lrec-conf.org/proceedings/lrec2018/pdf/160.pdf)

## Dataset Summary

A large corpus for sarcasm research and for training and evaluating systems for sarcasm detection is presented. The corpus comprises 1.3 million sarcastic statements, a quantity that is tenfold more substantial than any preceding dataset, and includes many more instances of non-sarcastic statements. This allows for learning in both balanced and unbalanced label regimes. Each statement is self-annotated; that is to say, sarcasm is labeled by the author, not by an independent annotator, and is accompanied by user, topic, and conversation context. The accuracy of the corpus is evaluated, benchmarks for sarcasm detection are established, and baseline methods are assessed.

For the details of this dataset, we refer you to the original [paper](http://www.lrec-conf.org/proceedings/lrec2018/pdf/160.pdf).

Metadata in Creative Language Toolkit ([CLTK](https://github.com/liyucheng09/cltk))
- CL Type: Sarcasm
- Task Type: detection
- Size: 1.3M
- Created time: 2018

### Contributions

If you have any queries, please open an issue or direct your queries to [mail](mailto:yucheng.li@surrey.ac.uk).

license:cc-by-2.0 region:us
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TAGS
#license-cc-by-2.0 #region-us 


# SARC_Sarcasm

## Dataset Description

- Paper: A Large Self-Annotated Corpus for Sarcasm

## Dataset Summary

A large corpus for sarcasm research and for training and evaluating systems for sarcasm detection is presented. The corpus comprises 1.3 million sarcastic statements, a quantity that is tenfold more substantial than any preceding dataset, and includes many more instances of non-sarcastic statements. This allows for learning in both balanced and unbalanced label regimes. Each statement is self-annotated; that is to say, sarcasm is labeled by the author, not by an independent annotator, and is accompanied by user, topic, and conversation context. The accuracy of the corpus is evaluated, benchmarks for sarcasm detection are established, and baseline methods are assessed.

For the details of this dataset, we refer you to the original paper.

Metadata in Creative Language Toolkit (CLTK)
- CL Type: Sarcasm
- Task Type: detection
- Size: 1.3M
- Created time: 2018

### Contributions

If you have any queries, please open an issue or direct your queries to mail.

# SARC_Sarcasm ## Dataset Description

- Paper: A Large Self-Annotated Corpus for Sarcasm ## Dataset Summary

A large corpus for sarcasm research and for training and evaluating systems for sarcasm detection is presented. The corpus comprises 1.3 million sarcastic statements, a quantity that is tenfold more substantial than any preceding dataset, and includes many more instances of non-sarcastic statements. This allows for learning in both balanced and unbalanced label regimes. Each statement is self-annotated; that is to say, sarcasm is labeled by the author, not by an independent annotator, and is accompanied by user, topic, and conversation context. The accuracy of the corpus is evaluated, benchmarks for sarcasm detection are established, and baseline methods are assessed.

For the details of this dataset, we refer you to the original paper.

Metadata in Creative Language Toolkit (CLTK)
- CL Type: Sarcasm
- Task Type: detection
- Size: 1.3M
- Created time: 2018 ### Contributions

If you have any queries, please open an issue or direct your queries to mail.
TAGS
#license-cc-by-2.0 #region-us 
 # SARC_Sarcasm ## Dataset Description

- Paper: A Large Self-Annotated Corpus for Sarcasm ## Dataset Summary

A large corpus for sarcasm research and for training and evaluating systems for sarcasm detection is presented. The corpus comprises 1.3 million sarcastic statements, a quantity that is tenfold more substantial than any preceding dataset, and includes many more instances of non-sarcastic statements. This allows for learning in both balanced and unbalanced label regimes. Each statement is self-annotated; that is to say, sarcasm is labeled by the author, not by an independent annotator, and is accompanied by user, topic, and conversation context. The accuracy of the corpus is evaluated, benchmarks for sarcasm detection are established, and baseline methods are assessed.

For the details of this dataset, we refer you to the original paper.

Metadata in Creative Language Toolkit (CLTK)
- CL Type: Sarcasm
- Task Type: detection
- Size: 1.3M
- Created time: 2018 ### Contributions

If you have any queries, please open an issue or direct your queries to mail.
passage: TAGS
#license-cc-by-2.0 #region-us 
# SARC_Sarcasm## Dataset Description

- Paper: A Large Self-Annotated Corpus for Sarcasm## Dataset Summary

A large corpus for sarcasm research and for training and evaluating systems for sarcasm detection is presented. The corpus comprises 1.3 million sarcastic statements, a quantity that is tenfold more substantial than any preceding dataset, and includes many more instances of non-sarcastic statements. This allows for learning in both balanced and unbalanced label regimes. Each statement is self-annotated; that is to say, sarcasm is labeled by the author, not by an independent annotator, and is accompanied by user, topic, and conversation context. The accuracy of the corpus is evaluated, benchmarks for sarcasm detection are established, and baseline methods are assessed.

For the details of this dataset, we refer you to the original paper.

Metadata in Creative Language Toolkit (CLTK)
- CL Type: Sarcasm
- Task Type: detection
- Size: 1.3M
- Created time: 2018### Contributions

If you have any queries, please open an issue or direct your queries to mail.