一个数据包用于抽象的意见总结,标题生成和基于评级的情绪预测,用于航空公司评论
Ayesha Ayub Syed1, Ford Lumban Gaol1, Alfred Boediman2
1Department of Doctor of Computer Science - BINUS Graduate Program, Bina Nusantara University, Jakarta, Indonesia.
Data in brief
|September 18, 2023
概括
这项研究引入了新的数据集,用于航空公司评论中的抽象意见总结和情绪分析. 这些资源旨在通过解决领域转移问题和实现多任务学习来改进自然语言处理模型.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 客户评论提供了有价值的见解,但由于长度和重复性,通常需要总结.
- 抽象的总结模型,当微调时,面临性能退化从域移动.
- 现有的数据集可能无法充分解决航空公司评论总结和情绪分析的具体挑战.
研究的目的:
- 引入一个全面的数据包,用于抽象的意见总结和相关的NLP任务.
- 为了解决在微调语言模型中的域移位问题,用于回顾总结.
- 为航空公司审查总结,标题生成和情绪分类提供基准数据集.
主要方法:
- 来自Skytrax的500个航空公司评论和摘要对的网络扫描,以进行抽象的总结.
- 收集了7079个评论-标题对,用于域名适应性培训和评论标题生成.
- 编制注释评论数据集,用于基于评级的情绪分类.
主要成果:
- 呈现了一个新的注释抽象总结数据集 (注释_abs_summ).
- 提供了一个用于域自适应训练的数据集 (review_titles_data).
- 一个注释数据集用于情感分析 (注释_情感) 发布,适合各种NLP任务.
结论:
- 发布的数据集可以作为基准和资源,用于改进抽象意见总结和情绪分析.
- 数据包有助于域调整和多任务学习,以提高NLP模型的性能.
- 这些数据集使得进一步的研究能够了解客户的意见,并预测审查属性.
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