Classification of Hope in Textual Data using Transformer-Based Models

TL;DR

This study compares transformer-based models for classifying hope in text, showing BERT's superior performance and efficiency, with implications for mental health and social media analysis.

Contribution

It introduces a comparative framework for hope classification using BERT, GPT-2, and DeBERTa, highlighting architecture-specific strengths and resource considerations.

Findings

BERT achieved over 84% binary accuracy.

BERT required less training time than newer models.

GPT-2 excelled at sarcasm detection with 92.46% recall.

Abstract

This paper presents a transformer-based approach for classifying hope expressions in text. We developed and compared three architectures (BERT, GPT-2, and DeBERTa) for both binary classification (Hope vs. Not Hope) and multiclass categorization (five hope-related categories). Our initial BERT implementation achieved 83.65% binary and 74.87% multiclass accuracy. In the extended comparison, BERT demonstrated superior performance (84.49% binary, 72.03% multiclass accuracy) while requiring significantly fewer computational resources (443s vs. 704s training time) than newer architectures. GPT-2 showed lowest overall accuracy (79.34% binary, 71.29% multiclass), while DeBERTa achieved moderate results (80.70% binary, 71.56% multiclass) but at substantially higher computational cost (947s for multiclass training). Error analysis revealed architecture-specific strengths in detecting nuanced hope expressions, with GPT-2 excelling at sarcasm detection (92.46% recall). This study provides a framework for computational analysis of hope, with applications in mental health and social media analysis, while demonstrating that architectural suitability may outweigh model size for specialized emotion detection tasks.