Abstract
People are able of transforming emotions into words, as a mechanism to communicate it. Additionally people are able to express emotions which can be grouped around specific interest centers. These two elements are considered as the basis for this work, which analyzes how people react when exposed to similar concepts. Different human groups are able to express themselves about a common phenomenon, by using different lexical elements. This work collects information from different geographic regions, considering an heterogeneous population. We present in this work the way people using a common language represent concepts which describe emotions depending on location and other variables, like educational level, gender and age, among others. The collection of the available lexicon is achieved through the use of the lexical availability methodology, supported by using neural networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bird, S., Loper E., Klein, E.: Natural Language Processing with Python. O’Reilly Media Inc. (2009)
Blanco, O., Salcedo, P., Kotz, G.: Lexical analysis of emotions: an approach using lexical availability and graph theory (in Spanish). Linguística y Literatura 78, 56–84 (2020)
Cellealta Barroso, F., Gallego Gallego D.: Medidas de disponibilidad léxica: comparabilidad y normalización (in Spanish). Boletín de Filología, vol. 511, Santiago, Chile (2016)
Echeverría, M., Urzúa, P., Figueroa, I.: Dispogen II. Programa computacional para el análisis de la disponiblidad léxica (in Spanish), Universidad de Concepción (2005)
Echeverría, M., Vargas, R., Urzúa, P., Ferreira, R.: Una nueva herramienta computacional para el análisis de relaciones semánticas en el léxico disponible (in Spanish). RLA, Revista de Linguística Teórica y Aplicada 46, 81–91 (2008)
Li, F., Zhang, X., Lu, A., Xu, L., Ren, D., You, T.: Estimation of metal elements content in soil using x-ray fluorescence based on multilayer perceptron. Environ. Monit. Assess. 194, 95 (2022)
Carmen, F.J., Natividad, H.M.: Revista electrónica de estudios hispánicos: Lexical and socionomastics availability (in Spanish). Ogigia. 25, 185–2010 (2018)
Górriz, J.M.: Artificial intelligence within the interplay between natural and artificial computation: advances in data science, trends and applications. Neurocomputing 410, 237–270 (2020)
Grunewald, U., Osorio, J.: To feel, to say, to do: expressive variety and emotion prototypes in the youth vocabulary. Onomazein 22, 125–163 (2010)
Kolagati, S., Priyadharshini, T., Mary Anita Rajam, V.: Exposing deepfakes using a deep multilayer perceptron - convolutional neural network model. Int. J. Inf. Manage. Data Insights 2(1), 100054 (2022)
Masip, D., Aran-Ramspott, S., Ruiz-Caballero, C., Suau, J., Almenar, E., Puertas-Graell, D.: Consumo informativo y cobertura mediática durante el confinamiento por el Covid-19: sobreinformación, sesgo ideológico y sensacionalismo (in Spanish). El Profesional de la información 29(3), 1–12 (2020). https://doi.org/10.3145/epi.2020.may.12
Picard, R.: Affective Computing for HCI. The MIT Press (1997)
Plutchik, R.: The nature of emotions: human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am. Sci. 89(4), 344–350 (2001)
Reeve, J.: Understanding Motivation and Emotion, 7th edn. Wiley (2018)
Salcedo, P., Morales-Candia, S., Fuentes-Riffo, K., Rivera-Robles, S., Sanhueza-Campos, C.: Teachers’ perception analysis on students’ emotion in virtual classes during covid-19 pandemic: a lexical availability approach. Sustainability 13(6413), 2021 (2021)
Kanti Karmaker, S., Hassan, M., Smith, M.J., Xu, L., Zhai, C., Veeramachaneni, K.: AutoML to date and beyond: challenges and opportunities. ACM Comput. Surv. 54(8), 1–36 (2022)
https://www.ibm.com/cl-es/products/spss-statistics. (visited January 2022)
https://cloud.google.com/automl. (visited January 2022)
Val-Calvo, M., Alvarez-Sánchez, J.R., Ferrández-Vicente, J.M., Fernández, E.: Affective-robot story-telling human-robot interaction: exploratory real-time emotion estimation analysis using facial expressions and physiological signals. IEEE Access 8, 134051–134066 (2020)
He, X., Zhao, K., Chu, X.: AutoML: a survey of the state-of-the-art. Knowl. Based Syst. 212, 106622 (2021)
Xu, Y., Li, F., Asgari, A.: Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms. Energy 240, 122692 (2022)
Zoeller, M.-A., Huber, M.F.: Benchmark and survey of automated machine learning frameworks. J. Artif. Intell. Res. 70, 122692 (2021)
Acknowledgement
This study has been partially supported by Project Fondecyt 1201572, National Agency for Research and Innovation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Salcedo-Lagos, P., Pinacho-Davidson, P., Pinninghoff, J.M.A., Kotz, G.G., Contreras, A.R. (2022). An Approach to Emotions Through Lexical Availability. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-031-06527-9_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06526-2
Online ISBN: 978-3-031-06527-9
eBook Packages: Computer ScienceComputer Science (R0)