Abstract

This dataset presents a curated collection of 9,000 English verbs annotated with normalized fuzzy values across four cognitive-behavioral quadrants of the BEET-M (Behavior Engagement Emotion Trigger Modes) model: Value & Credibility (NW), Relationship & Human Impact (NE), Process & Information (SE), and Time Urgency (SW). Each verb is assigned fuzzy scores summing to 1.0, along with a corresponding binary vector marking its dominant influence quadrant. This dataset supports applications in affective computing, persuasive communication, human-computer interaction, and natural language processing, particularly for emotionally adaptive systems and context-aware word scoring.

Instructions:

The dataset contains 9,000 rows with the following columns:

ColumnDescriptionVerbBase form of an English verbNW_fuzzyNormalized fuzzy score for Value & Credibility quadrantNE_fuzzyNormalized fuzzy score for Relationship & Human Impact quadrantSE_fuzzyNormalized fuzzy score for Process & Information quadrantSW_fuzzyNormalized fuzzy score for Time Urgency quadrantNW_binary1 if NW has the highest fuzzy score for the verb, else 0NE_binary1 if NE is dominantSE_binary1 if SE is dominantSW_binary1 if SW is dominant

All fuzzy scores are generated using a controlled random distribution normalized to sum to 1. Binary flags are computed using argmax() logic.

Experimental Design, Materials and Methods

Data Source

We extracted verb forms from the open-source verbs-all.csv dataset, which includes base forms and conjugations. After deduplication and cleaning, the first column of each row (base form) was selected.