ABSTRACT

Student recruitment and retention heavily depend on student satisfaction and word of mouth voiced through surveys, social media, and review websites. The amount of online reviews makes their manual analysis intractable, revealing the need for automated approaches. The paper presents the first Serbian language corpus manually annotated for opinions in the domain of higher education. Agreement among annotators was calculated using the Fleiss’ kappa and agr metrics. Statistical and linguistic analyses of the corpus revealed information useful for hand-crafted rules for sentiment analysis. Using developed corpus, dictionary- and machine learning-based approaches to sentiment analysis are benchmarked. Both exhibit high performance.

摘要

学生的招募和保留在很大程度上取决于学生的满意度和通过调查、社交媒体和评论网站表达的口碑。在线评论的数量使他们的手动分析变得棘手，揭示了对自动化方法的需求。本文介绍了第一个手动注释的塞尔维亚语语料库，用于高等教育领域的意见。使用 Fleiss 的 kappa 和 agr 指标计算注释者之间的一致性。语料库的统计和语言分析揭示了对情绪分析的手工规则有用的信息。使用开发的语料库、基于字典和机器学习的情感分析方法进行基准测试。两者都表现出高性能。