The NRC Valence, Arousal, and Dominance (VAD) Lexicon includes a list of more than 20,000 English words and their valence, arousal, and dominance scores. For a given word and a dimension (V/A/D), the scores range from 0 (lowest V/A/D) to 1 (highest V/A/D). The lexicon with its fine-grained real- valued scores was created by manual annotation using best--worst scaling. The lexicon is markedly larger than any of the existing VAD lexicons. We also show that the ratings obtained are substantially more reliable than those in existing lexicons.
Usage
lexicon_nrc_vad(
dir = NULL,
delete = FALSE,
return_path = FALSE,
clean = FALSE,
manual_download = FALSE
)
Arguments
- dir
Character, path to directory where data will be stored. If
NULL
, user_cache_dir will be used to determine path.- delete
Logical, set
TRUE
to delete dataset.- return_path
Logical, set
TRUE
to return the path of the dataset.- clean
Logical, set
TRUE
to remove intermediate files. This can greatly reduce the size. Defaults to FALSE.- manual_download
Logical, set
TRUE
if you have manually downloaded the file and placed it in the folder designated by running this function withreturn_path = TRUE
.
Value
A tibble with 20.007 rows and 4 variables:
- word
An English word
- Valence
valence score of the word
- Arousal
arousal score of the word
- Dominance
dominance score of the word
Details
License required for commercial use. Please contact Saif M. Mohammad (saif.mohammad@nrc-cnrc.gc.ca).
Citation info:
Details of the NRC VAD Lexicon are available in this paper:
Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. Saif M. Mohammad. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, July 2018.
inproceedings{vad-acl2018,
title={Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words},
author={Mohammad, Saif M.},
booktitle={Proceedings of The Annual Conference of the Association for Computational Linguistics (ACL)},
year={2018},
address={Melbourne, Australia}
}
See also
Other lexicon:
lexicon_afinn()
,
lexicon_bing()
,
lexicon_loughran()
,
lexicon_nrc()
,
lexicon_nrc_eil()