TY - JOUR
T1 - EmoDNN: Understanding Emotions From Short Texts Through a Deep Neural Network Ensemble
AU - Kamran, Sara
AU - Zall, Raziyeh
AU - Hosseini, Saeid
AU - Kangavari, Mohammad Reza
AU - Rahmani, Sana
AU - Hua, Wen
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2023/6
Y1 - 2023/6
N2 - The knowledge obtained from emotions via online communities is substantially valuable in various domains, including social management, resource planning, politics, and market predictions. Affective computing, as a multi-aspect realm, aims to exploit emotion-pertinent details from various contents via connecting artificial intelligence to cognitive science. The hidden personality cues in daily brief contents can reveal the cognitive aspect of authors and uncover both similarities and contrasts between them. However, the main challenge lies in devising a cognition-aware algorithm to trace emotional cues in brief contents. To solve the challenge, we develop a novel framework that infers the cognitive aspect of individuals. We propose a deep ensemble method, supplied with a novel dropout algorithm, that aggregates outcomes from various classifiers to extract emotions from short texts. We employ a new embedding approach to enrich emotion-relevant features, collectively assembled via lexicons and attention actuates, resulting in a preferable set of vectors. The experimental results show that our proposed framework can achieve better accuracy in recognizing emotions versus other trending competitors. We empirically observe that detecting emotion latent cues via relying on personality features can effectively distinguish short text authors. Furthermore, the deep learning models overcome conventional methods, including the SVM, categorization, and heuristic rules.
AB - The knowledge obtained from emotions via online communities is substantially valuable in various domains, including social management, resource planning, politics, and market predictions. Affective computing, as a multi-aspect realm, aims to exploit emotion-pertinent details from various contents via connecting artificial intelligence to cognitive science. The hidden personality cues in daily brief contents can reveal the cognitive aspect of authors and uncover both similarities and contrasts between them. However, the main challenge lies in devising a cognition-aware algorithm to trace emotional cues in brief contents. To solve the challenge, we develop a novel framework that infers the cognitive aspect of individuals. We propose a deep ensemble method, supplied with a novel dropout algorithm, that aggregates outcomes from various classifiers to extract emotions from short texts. We employ a new embedding approach to enrich emotion-relevant features, collectively assembled via lexicons and attention actuates, resulting in a preferable set of vectors. The experimental results show that our proposed framework can achieve better accuracy in recognizing emotions versus other trending competitors. We empirically observe that detecting emotion latent cues via relying on personality features can effectively distinguish short text authors. Furthermore, the deep learning models overcome conventional methods, including the SVM, categorization, and heuristic rules.
KW - Cognitive factors
KW - Emotion recognition
KW - Ensemble learning
KW - Neural network architecture
UR - http://www.scopus.com/inward/record.url?scp=85150022024&partnerID=8YFLogxK
U2 - 10.1007/s00521-023-08435-x
DO - 10.1007/s00521-023-08435-x
M3 - Journal article
AN - SCOPUS:85150022024
SN - 0941-0643
VL - 35
SP - 13565
EP - 13582
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 18
ER -