Date of Award

6-2-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Aryabrata Basu

Abstract

While sentiment analysis has made significant strides in domains such as social media and personal correspondence, its application to formal scientific writings remains under-explored. The crosstalk between emotional expressions in personal and professional communications has also received limited attention despite its potential to reveal insights into the emotional drivers of scientific creativity. Our research introduces a computational framework designed to detect and quantify emotional expressions across various documents over time. Leveraging state-of-the-art transformer models fine-tuned on domain-specific corpora, the framework models emotional tone distribution. Integrating emotion analysis with knowledge graph modeling enables the exploration of emotional trends alongside key scientific milestones. Temporal analysis techniques allow us to further track emotional shifts alongside significant research developments. Our research contributes to computational linguistics and sentiment-aware AI systems by addressing the gap in sentiment analysis within formal texts and uncovering the under-explored crosstalk of ideas between personal and professional writings.

Download

Share

COinS