map_discrete_to_vad: Map Discrete Emotions to VAD (Valence-Arousal-Dominance)...

In transforEmotion: Sentiment Analysis for Text, Image and Video using Transformer Models

Map Discrete Emotions to VAD (Valence-Arousal-Dominance) Framework

Description

Maps discrete emotion classifications from image_scores(), transformer_scores(), or video_scores() functions to the Valence-Arousal-Dominance (VAD) framework using published lexicons. Automatically downloads the NRC VAD lexicon via textdata package on first use.

Usage

map_discrete_to_vad(
  results,
  mapping = "nrc_vad",
  weighted = TRUE,
  cache_lexicon = TRUE,
  vad_lexicon = NULL
)

Arguments

results	Output from image_scores(), transformer_scores(), or video_scores(). Can be a data.frame (from image/video functions) or a list (from transformer functions).
mapping	Character. Which VAD mapping to use. Currently supports: "nrc_vad": Uses NRC VAD lexicon (Mohammad, 2018)
weighted	Logical. Whether to compute weighted averages based on confidence scores (default: TRUE). If FALSE, performs simple lookup of the highest-scoring emotion.
cache_lexicon	Logical. Whether to cache the VAD lexicon for repeated use (default: TRUE).
vad_lexicon	Optional data.frame. Pre-loaded VAD lexicon data to use instead of loading from textdata. Must have columns for word, valence, arousal, dominance (accepts both lowercase and capitalized versions, e.g., Word/word, Valence/valence). If provided, the function will use this data directly.

Details

This function bridges discrete emotion classification outputs with the continuous VAD emotion framework. The VAD model represents emotions in a three-dimensional space where:

• **Valence**: Pleasantness (positive/negative)

• **Arousal**: Activation level (excited/calm)

• **Dominance**: Control (dominant/submissive)

**Input Type Detection:** The function automatically detects the input type:

• **data.frame**: Assumes output from image_scores() or video_scores()

• **list**: Assumes output from transformer_scores()

**Weighting Methods:**

• **weighted = TRUE**: Computes weighted average VAD scores based on classification confidence scores

• **weighted = FALSE**: Uses VAD values for the highest-scoring emotion only

**VAD Mappings:** Currently supports the NRC VAD lexicon which provides VAD ratings for emotion words based on crowdsourced annotations. The lexicon must be downloaded first using 'textdata::lexicon_nrc_vad()' in an interactive session.

**Setup Required:** Before using this function, download the NRC VAD lexicon by running: 'textdata::lexicon_nrc_vad()' in an interactive R session and accepting the license.

Value

A data.frame with columns:

• valence: Valence score (positive vs negative emotion)

• arousal: Arousal score (excitement vs calmness)

• dominance: Dominance score (control vs submissiveness)

For transformer_scores() input, includes additional identifier columns. For image/video_scores() input, returns one row per input row.

Data Privacy

VAD lexicon is downloaded once and cached locally. No data is sent to external servers.

Author(s)

Aleksandar Tomasevic <[e-mail]>

References

Mohammad, S. M. (2018). Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 174-184.

Examples

## Not run: 
# Method 1: Auto-load from textdata (requires prior download)
textdata::lexicon_nrc_vad()  # Run once to download

# With image scores
image_path <- system.file("extdata", "boris-1.png", package = "transforEmotion")
emotions <- c("joy", "sadness", "anger", "fear", "surprise", "disgust")
img_results <- image_scores(image_path, emotions)
vad_results <- map_discrete_to_vad(img_results)

# Method 2: Download once and pass as argument (recommended)
nrc_vad <- textdata::lexicon_nrc_vad()  # Download once

# Use with different emotion results
vad_results1 <- map_discrete_to_vad(img_results, vad_lexicon = nrc_vad)

text <- "I am so happy today!"
trans_results <- transformer_scores(text, emotions)
vad_results2 <- map_discrete_to_vad(trans_results, vad_lexicon = nrc_vad)

# Simple lookup (no weighting)
vad_simple <- map_discrete_to_vad(img_results, weighted = FALSE, vad_lexicon = nrc_vad)

## End(Not run)

transforEmotion documentation built on Jan. 8, 2026, 5:06 p.m.