Social context effects on emotional language: The influence of the COVID-19 pandemic on the emotional evaluation of words.

The COVID-19 pandemic has altered our routines, our conversations, the specific social contexts in which we hear or use certain words, and potentially, the representation of the words related to the disease and its consequences. Here we investigated whether the effects of the pandemic have changed the representation of the affective features of COVID-19-related words. To this aim, we collected new ratings of valence (from unpleasant to pleasant) and arousal (from calm to activated) dimensions for COVID-19-related words (e.g., hospital) and COVID-19-unrelated words (e.g., whale). Subsequently, we compared these scores with those from databases that reported ratings for the same pool of words before the pandemic. Our results showed significant changes in arousal for COVID-19-related words but not unrelated words, thus revealing that the pandemic social context modified their affective representation. These findings support the flexibility of emotional representations and the malleability and dynamicity of the mental lexicon as a function of contextual factors.

The Nature of Word Associations in Sentence Contexts.

How words are interrelated in the human mind is a scientific topic on which there is still no consensus, with different views on how word co-occurrence and semantic relatedness mediate word association. Recent research has shown that lexical associations are strongly predicted by the similarity of those words in terms of valence, arousal, and concreteness ratings. In the current study, we aimed at extending these results to more complex and realistic linguistic scenarios, since human communication is not done with word pairs, but rather through sentences. Hence, the aim of the current study was to verify whether valence, arousal, and concreteness also articulate sentence-level lexical representations. To this end, 32 native Spanish speakers were given cue words and asked to use them in sentences that would provide a meaningful context. The content words of the written sentences were then analyzed. Our results showed that the emotional dimensions (valence and arousal) and concreteness values of the cue words effectively predicted the same values of said dimensions of their sentences' words. In sum, the similarity in the emotional dimensions and concreteness are crucial mechanisms behind word association in the human mind.

Valence, Arousal and Concreteness Mediate Word Association.

BACKGROUND: Given the impact of lexical properties such as valence, arousal, and concreteness in language processing, recent computational methods have been designed to extrapolate these values from different sources, such as word co-occurrence or word association corpora. These methods have been proven to be particularly successful approaches to extract lexical features from word association data. Consequently, valence, arousal and concreteness seem to be represented in word association, and we hypothesize that they might in fact be critical mediators in the process. METHOD: To test our hypothesis, we paired the cue and associate words from three databases in three different languages with their valence, arousal and concreteness values. We then conducted linear regression analyses to see if an associate's score in each dimension could be predicted by the scores of its cue word. RESULTS: The analyses showed that the score of the cue words in each of the three dimensions was a strong predictor of the scores of their associates in the same dimension. Furthermore, words that were more strongly associated tended to have more similar scores. CONCLUSIONS: We showed that across different languages, word association is mediated and can be predicted by concreteness, arousal and valence.

Valence, Arousal and Concreteness Mediate Word Association / La Valencia, la Activación y la Concreción Median la Asociación dePalabras

Background: Given the impact of lexical properties such as valence, arousal, and concreteness in language processing, recent computational methods have been designed to extrapolate these values from different sources, such as word co-occurrence or word association corpora. These methods have been proven to be particularly successful approaches to extract lexical features from word association data. Consequently, valence, arousal and concreteness seem to be represented in word association, and we hypothesize that they might in fact be critical mediators in the process. Method: To test our hypothesis, we paired the cue and associate words from three databases in three different languages with their valence, arousal and concreteness values. We then conducted linear regression analyses to see if an associate’s score in each dimension could be predicted by the scores of its cue word. Results: The analyses showed that the score of the cue words in each of the three dimensions was a strong predictor of the scores of their associates in the same dimension. Furthermore, words that were more strongly associated tended to have more similar scores. Conclusions: We showed that across different languages, word association is mediated and can be predicted by concreteness, arousal and valence. (AU)

Antecedentes: dado el impacto de las propiedades léxicas como la valencia, la activación y la concreción en el procesamiento del lenguaje, se han diseñado métodos computacionales recientes para extrapolar estos valores de diferentes fuentes. Estos métodos son particularmente exitosos para extraer características léxicas de bases de asociación de palabras. En consecuencia, la valencia, la activación y la concreción parecen estar representadas en la asociación de palabras. Hipotetizamos que, de hecho, podrían ser mediadores críticos en el proceso. Método: para probar nuestra hipótesis, emparejamos la señal y sus asociados en tres bases de datos en tres idiomas diferentes con sus valores de valencia, activación y concreción. Realizamos análisis de regresión lineal para explorar si la puntuación de un asociado en cada dimensión podría predecirse mediante las puntuaciones de su palabra señal. Resultados: los análisis mostraron que la puntuación de las palabras señal en cada una de las tres dimensiones es un fuerte predictor de las puntuaciones de sus asociados en la misma dimensión. Además, las palabras que estaban más fuertemente asociadas tendían a tener puntuaciones más similares. Conclusiones: demostramos que en diferentes lenguas la asociación de palabras está mediada y puede predecirse por la concreción, la activación y la valencia. (AU)