Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words
Words play a central role in language and thought. Factor analysis studies have shown that the primary dimensions of meaning are valence, arousal, and dominance (VAD). We present the NRC VAD Lexicon, which has human ratings of valence, arousal, and dominance for more than 20,000 English words. We use Best{--}Worst Scaling to obtain fine-grained scores and address issues of annotation consistency that plague traditional rating scale methods of annotation. We show that the ratings obtained are vastly more reliable than those in existing lexicons. We also show that there exist statistically significant differences in the shared understanding of valence, arousal, and dominance across demographic variables such as age, gender, and personality.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionSimilar Papers 제목 키워드 기반
Using large language models to estimate features of multi-word expressions: Concreteness, valence, arousal
This study investigates the potential of large language models (LLMs) to provide accurate estimates of concreteness, valence and arousal for multi-word expressions. Unlike previous artificial intelligence (AI) methods, L…
Breaking Bad: Norms for Valence, Arousal, and Dominance for over 10k English Multiword Expressions
Factor analysis studies have shown that the primary dimensions of word meaning are Valence (V), Arousal (A), and Dominance (D). Existing lexicons such as the NRC VAD Lexicon, published in 2018, include VAD association ra…
NRC VAD Lexicon v2: Norms for Valence, Arousal, and Dominance for over 55k English Terms
Factor analysis studies have shown that the primary dimensions of word meaning are Valence (V), Arousal (A), and Dominance (D) (also referred to in social cognition research as Competence (C)). These dimensions impact va…
Automatically Generated Affective Norms of Abstractness, Arousal, Imageability and Valence for 350 000 German Lemmas
This paper presents a collection of 350,000 German lemmatised words, rated on four psycholinguistic affective attributes. All ratings were obtained via a supervised learning algorithm that can automatically calculate a n…
AllTHU\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM
Predicting valence-arousal ratings for words and phrases is very useful for constructing affective resources for dimensional sentiment analysis. Since the existing valence-arousal resources of Chinese are mainly in word-…
Opinion MiningPOSSentiment AnalysisTask 2