University of Illinois at Chicago
Browse

File(s) under embargo

1

year(s)

3

month(s)

7

day(s)

until file(s) become available

Exploring Language-appropriate Inductive Biases for Emotion Detection

thesis
posted on 2023-08-01, 00:00 authored by Tiberiu Sosea
Emotions are an integral element of human nature and are expressed in various situations in one’s life. These situations can stir a diverse range of feelings and emotions, e.g., from fear and sadness to joy and trust. When facing these circumstances, many people turn to online social platforms to express their emotions. Modeling emotions is extremely difficult and therefore emotion-related tasks are vital for assessing and improving the language understanding capabilities of large language models. This dissertation focuses on improving the understanding of emotions at scale. We propose novel algorithms and deep learning approaches to (1) detect expressions of emotions (although often not explicit) in a text and characterize their intensity and polarity; (2) identify the triggers (e.g., the topic, situation, or event) causing the emotions; and (3) predict how the emotions change over time and analyze emotion deviation (i.e., the varied emotions that people express towards the same trigger, e.g., two opposing signals – elation and sadness or rage – towards the same event).

History

Advisor

Caragea, Cornelia

Chair

Caragea, Cornelia

Department

Computer Science

Degree Grantor

University of Illinois at Chicago

Degree Level

  • Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Sidiropoulos, Anastasios Li, Junyi Caragea, Doina Parde, Natalie

Submitted date

August 2023

Thesis type

application/pdf

Language

  • en

Usage metrics

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC