一种用于工科学生问题与津贴分类的混合分类算法
A Hybrid Classification Algorithm to Classify Engineering Students' Problems and Perks
摘要：
社交网站为表达个人观点和意见带来了新的视野。此外，他们还提供媒介，让学生分享他们的情感，包括学习过程中的斗争和喜悦。这种非正式的信息为决策提供了一个很好的场所。信息的规模越来越大，需要自动分类技术。情感分析是对大量数据进行分类的自动化技术之一。现有的预测性情感分析技术被广泛用于对电子商务网站上的评论进行分类，以提供商业智能。然而，它们对教育系统的决策没有多大用处，因为它们将情绪分为三个预设类别：积极的，消极的和中性的。此外，将学生的情绪分为积极或消极的类别并不能更深入地了解他们的问题和津贴。本文提出了一种新的混合分类算法来对工科学生的情感进行分类。与传统的预测性情感分析技术不同，该算法使情感分析过程具有描述性。此外，它将工程专业学生的津贴和问题分为几类，以帮助未来的学生和教育系统的决策。
英文摘要：
The social networking sites have brought a new horizon for expressing views and opinions of individuals. Moreover, they provide medium to students to share their sentiments including struggles and joy during the learning process. Such informal information has a great venue for decision making. The large and growing scale of information needs automatic classification techniques. Sentiment analysis is one of the automated techniques to classify large data. The existing predictive sentiment analysis techniques are highly used to classify reviews on E-commerce sites to provide business intelligence. However, they are not much useful to draw decisions in education system since they classify the sentiments into merely three preset categories: positive, negative and neutral. Moreover, classifying the students sentiments into positive or negative category does not provide deeper insight into their problems and perks. In this paper, we propose a novel Hybrid Classification Algorithm to classify engineering students sentiments. Unlike traditional predictive sentiment analysis techniques, the proposed algorithm makes sentiment analysis process descriptive. Moreover, it classifies engineering students perks in addition to problems into several categories to help future students and education system in decision making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
