Score Text Valence
score_valence.RdScore text responses using up to 8 sentiment dictionaries.
Always applies Lexicoder (LSD2015), NRC, Bing, AFINN, and Loughran. By
default also applies SentiWordNet, Warriner et al. (2013) affective norms,
and the Jockers/Rinker lexicon. The Bing/NRC/AFINN/Loughran/Lexicoder/
SentiWordNet dictionaries score each text as -1, 0, or +1 (presence-based).
Warriner and Jockers are continuous-valued in [-1, 1] (mean of matched-word
valences; Warriner's 1-9 ratings are linearly rescaled, Jockers values
clipped to [-1, 1]). All per-dictionary scores are then averaged into the
combined negation-aware ValenceYesNA / ValenceNoNA.
Usage
score_valence(
data,
text_col = "tv",
response_col = NULL,
include_sentiwn = TRUE,
sentiwn_threshold = 0.1,
include_warriner = TRUE,
include_jockers = TRUE
)Arguments
- data
A data.frame with text to score
- text_col
Column containing cleaned text for valence scoring (default "tv"). Uses tv (not tv2/tv3) to preserve original response for LLMs with low spelling errors. Change to "tv2" or "tv3" for human participants (a singularized text column matches more dictionary entries when responses contain plural/inflected forms).
- response_col
Column used for NA-gating (default: same as text_col). Only needed if your NA-indicator column differs from text_col.
- include_sentiwn
Logical (default
TRUE). IfTRUE, also apply SentiWordNet (presence-based, thresholded atsentiwn_threshold) and addVal_sentiwn/Val_sentiwnNA.- sentiwn_threshold
Numeric in [0, 1] (default 0.10). Minimum |Val_sentiwn| magnitude for a word to count as positive or negative.
- include_warriner
Logical (default
TRUE). IfTRUE, also apply Warriner et al. (2013) affective norms (continuous valence in [-1, 1] from 13,915 lemmas, rescaled from the original 1-9 Likert) and addVal_warriner/Val_warrinerNA.- include_jockers
Logical (default
TRUE). IfTRUE, also apply the Jockers/Rinker lexicon (continuous valence clipped to [-1, 1] from 11,710 entries) and addVal_jockers/Val_jockersNA.