---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: author
    dtype: string
  - name: score
    dtype: int64
  - name: ups
    dtype: int64
  - name: downs
    dtype: int64
  - name: date
    dtype: string
  - name: created_utc
    dtype: int64
  - name: subreddit
    dtype: string
  - name: id
    dtype: string
  splits:
  - name: train
    num_bytes: 1764500045
    num_examples: 12704751
  download_size: 903559115
  dataset_size: 1764500045
license: cc-by-2.0
---

# 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).