Publication Date

Spring 2026

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Robert Chun

Second Advisor

William Andreopoulos

Third Advisor

Navrati Saxena

Keywords

emotion detection, conversational networks, graph neural networks, homophily, hop-based similarity, temporal decay

Abstract

Most social media datasets for emotion detection have not been constructed to account for conversational structure‚ so we investigate whether it carries signal for emotion prediction. Using Sentiment140 and GoEmotions Reddit threads‚ we construct their thread-based conversation graphs and compute the aggregated features of neighbors as well as the transformer and TF-IDF representations of comments. Connected comments are 4.5× more similar in emotions than expected by chance. Pairwise emotional similarity decays exponentially with geodesic distance (e.g.‚ after 2 hops). Pairs separated by a 30s timestamp difference have the highest emotional similarity. These results suggest that emotion is structured locally and temporally within a conversation. Although predictive gains by conversational graph context are limited by the sparsity of features‚ structural results show conversational graph structure captures meaningful emotional dynamics not present with text-only approaches.

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