论文标题
针对土耳其文本的目标情绪分析的基于数据集和基于BERT的模型
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts
论文作者
论文摘要
有针对性的情感分析旨在从给定文本中提取对特定目标的情感。由于互联网的可访问性越来越高，该领域引起了人们的注意，这导致人们产生大量数据。情感分析通常需要注释的培训数据，是一个经过精心研究的领域，用于广泛研究的语言，例如英语。对于诸如土耳其语之类的低资源语言，缺乏这样的注释数据。我们提出了一个带注释的土耳其数据集，适用于目标情绪分析。我们还提出了具有不同体系结构的基于BERT的模型，以完成目标情感分析的任务。结果表明，所提出的模型优于目标情感分析任务的传统情感分析模型。
Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text. It is a field that is attracting attention due to the increasing accessibility of the Internet, which leads people to generate an enormous amount of data. Sentiment analysis, which in general requires annotated data for training, is a well-researched area for widely studied languages such as English. For low-resource languages such as Turkish, there is a lack of such annotated data. We present an annotated Turkish dataset suitable for targeted sentiment analysis. We also propose BERT-based models with different architectures to accomplish the task of targeted sentiment analysis. The results demonstrate that the proposed models outperform the traditional sentiment analysis models for the targeted sentiment analysis task.
