Dissertation/ Thesis

Emotion recognition and flip reasoning in english and mixed-coded con-versations based on a valence, arousal and dominance approach

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Emotion recognition and flip reasoning in english and mixed-coded con-versations based on a valence, arousal and dominance approach
Συγγραφείς: García Bedoya, Santiago Andre
Συνεισφορές: Martínez Santos, Juan Carlos, Acevedo Patiño, Óscar, Puertas Del Castillo, Edwin Alexander
Στοιχεία εκδότη: Universidad Tecnológica de Bolívar UTB, 2024.
Έτος έκδοσης: 2024
Θεματικοί όροι: Machine learning, Emotions, Engineering -- Statistics, Computational linguistics, Mathematical statistics -- Data processing, Python
Περιγραφή: Using the NRC VAD Lexicon and computational models like Transformer and GRU, this study presents a novel method for emotion recognition and reasoning about emo- tional transitions in code-mixed talks. We use Emotion Flip Reasoning (EFR) and Emotion Recognition in Conversation (ERC) to systematically identify and classify emotional triggers. We validate the model’s accuracy in identifying emotional shift triggers and classifying emotions using the MELD and MaSaC datasets provided by SemEval. Including VAD analysis significantly improves accuracy, as indicated by an increase in the F1 score. These findings open up new avenues for the study of emotional dynamics in texts with mixed codes by highlighting the significance of including complex emotional elements in conversation analysis. Finally, submit the results and manuscript to SemEval 2024 Competition.
Ingeniero Electrónico
Pregrado
Τύπος εγγράφου: Bachelor thesis
Περιγραφή αρχείου: 83 páginas.; application/PDF
Γλώσσα: English
Σύνδεσμος πρόσβασης: https://utb.alma.exlibrisgroup.com/discovery/delivery/57UTB_INST:57UTB_INST/1242716950005731
https://hdl.handle.net/20.500.12585/13916
Rights: CC BY NC ND
Αριθμός Καταχώρησης: edsair.od......9623..eb3387b3f1dc75c4ac5ca946d242971b
Βάση Δεδομένων: OpenAIRE