论文标题
教育领域的基于方面的情感分析
Aspect-Based Sentiment Analysis in Education Domain
论文作者
论文摘要
大量数据的分析始终为机构和组织带来了价值。最近，通过文本表达的人们的意见已成为该分析的一个非常重要的方面。为了应对这一挑战，已经出现了一种称为基于方面的情感分析（ABSA）的自然语言处理技术。 Absa具有分别为意见的各个方面提取极性的能力，Absa发现自己在广泛的领域中有用。教育是可以成功利用ABSA的领域之一。能够理解并找出学生对课程，教授或教学方法的最喜欢和不喜欢什么，这对于各自的机构可能非常重要。尽管此任务代表了一个独特的NLP挑战，但许多研究提出了解决该问题的不同方法。在这项工作中，我们对ABSA现有工作的现有工作进行了全面审查，重点是教育领域。讨论了广泛的方法，并得出结论。
Analysis of a large amount of data has always brought value to institutions and organizations. Lately, people's opinions expressed through text have become a very important aspect of this analysis. In response to this challenge, a natural language processing technique known as Aspect-Based Sentiment Analysis (ABSA) has emerged. Having the ability to extract the polarity for each aspect of opinions separately, ABSA has found itself useful in a wide range of domains. Education is one of the domains in which ABSA can be successfully utilized. Being able to understand and find out what students like and don't like most about a course, professor, or teaching methodology can be of great importance for the respective institutions. While this task represents a unique NLP challenge, many studies have proposed different approaches to tackle the problem. In this work, we present a comprehensive review of the existing work in ABSA with a focus in the education domain. A wide range of methodologies are discussed and conclusions are drawn.
