用Transformer模型识别文本中的希望表达，效果优于传统方法。
Classification of Hope in Textual Data using Transformer-Based Models
- 对比BERT、GPT-2、DeBERTa三类模型分类希望情绪
- BERT在二分类达84.49%准确率，训练时间仅443秒
- 发现模型架构比大小更影响特定情绪识别效果
本文提出基于Transformer的文本希望表达分类方法，比较了BERT、GPT-2和DeBERTa三种模型在二分类（希望 vs 非希望）和五类多分类任务上的表现。初始BERT实现达到83.65%二分类与74.87%多分类准确率；扩展对比中，BERT表现最优（84.49%二分类，72.03%多分类），训练时间仅443秒，显著低于其他模型（如DeBERTa需947秒）。GPT-2二分类准确率为79.34%，多分类71.29%，但其在讽刺语气检测中表现突出（召回率92.46%）。研究为希望的计算分析提供框架，适用于心理健康与社交媒体监测，表明针对特定情绪任务，模型架构适配性可能优于规模。
原文摘要 · Abstract (English)
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.
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