使用BERT和SO-PMI构建一个特定领域的情绪词典,用于农产品评论
Jinghua Wu1, Peng Qiu1, Xun Jia1
1College of Information Engineering, Sichuan Agricultural University, Sichuan Ya'an, China.
PloS one
|June 26, 2025
概括
本研究介绍了BSTS情绪词典,这是分析农产品评论的专门工具. 它显著提高了在这一领域的情绪分类准确性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 情绪分析 情绪分析
背景情况:
- 一般情绪词典缺乏农产品评价的特定领域特征.
- 现有的工具导致这个利基领域的情绪分类表现不足.
研究的目的:
- 为农产品评价开发一个特定领域的情感词典.
- 提高农业电子商务部门情绪分析的准确性和有效性.
主要方法:
- 使用19,843个JD.com评论构建了BSTS情绪词典.
- 利用基于BERT的术语频率 (TF) 分析来提取种子词.
- 应用了SO-PMI算法来计算情绪得分并确定最佳值.
主要成果:
- 在JD.com评论中,BSTS词典获得了85.21%的精度,91.92%的回忆率和88.44%的F1分.
- 在淘宝评论上表现出稳健性,准确率为93.28%,F1得分为87.34%.
- 在农产品审查分类中表现优于现有的通用情绪词典.
结论:
- BSTS词典为特定领域的情绪分析提供了可靠和有效的工具.
- 显著提高农产品的情绪分类准确性.
- 在不同的电子商务平台上验证有效性.
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