TriLex:一种融合方法,用于对短文本进行无监督的情感分析
Abdulrahman Alharbi1,2, Rafaa Aljurbua1,3, Shelly Gupta1
1Department of Computer and Information Sciences, Temple University, Philadelphia, Pennsylvania, United States of America.
PloS one
|April 17, 2025
概括
这项研究介绍了TriLex,这是一种用于短文本的无监督情感分析方法. TriLex通过结合多个词汇工具,将情绪预测准确度提高2%-8%.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 在线评论和社交媒体产生了大量的短文数据.
- 在短文中分析情绪是由于上下文限制而具有挑战性的.
- 传统的监督方法与动态情绪和数据稀缺性作斗争.
研究的目的:
- 提出TriLex,一个新的无监督方法来分析短文的情感分析.
- 解决情绪分析中传统监督方法的局限性.
- 提高用户生成内容情绪分析的准确性和效率.
主要方法:
- TriLex使用来自多个词典基础情感分析工具 (TextBlob,VADER,AFINN) 的多数选票.
- 它使情绪得分正常化,并应用加权平均计算来计算多数票.
- 一个动态值根据大多数人对改善情绪标签的投票来改进弱标签.
主要成果:
- 在基准数据集上,TriLex模型显示了更好的准确性,F1得分,精度和回忆.
- 评估的模型 (逻辑回归,XGBoost,LSTM) 使用TriLex.ex显示了2%-8%的精度改善.
- TriLex的表现优于单个基于词典的方法和现有的基于融合的替代方法.
结论:
- TriLex提供了一种有效的无监督解决方案,用于对短文本的情感分析.
- 拟议的方法提高了情绪预测的准确性,并解决了数据稀缺性挑战.
- TriLex代表了从在线平台分析用户情绪的重大进步.
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