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Aspect based sentiment analysis of students opinion using machine learning techniques

机译：使用机器学习技术的基于方面的学生意见情感分析

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摘要

Recent times, customer wants to share good and bad opinions about their experience like usage of recently purchased product, services provided by a hospital, education and so on over social media, micro blogs, review sites and etc. Today smart phones become mandatory for most of the college students. They share their experience and feelings immediately over internet applications with others. Student opinions can be collected through the internet applications and can be categorized based on various entities. We propose a new method of analyzing online student feedback collected from twitter API by measuring semantic relatedness between aspect word and student opinion sentence. The results of this work will help the students to improve their studies and helps the instructors to improve their teaching skills. In this work classification and clustering techniques have been used to categorize the opinions.
机译：最近，客户希望通过社交媒体，微博客，评论站点等，就他们的经历（例如最近购买的产品的使用，医院提供的服务，教育等）分享好与坏的看法。如今，智能手机已成为大多数人的必修课的大学生。他们立即通过Internet应用程序与他人分享他们的经验和感受。可以通过互联网应用程序收集学生意见，并可以基于各种实体对其进行分类。我们提出了一种通过测量方面词与学生意见句之间的语义相关性来分析从twitter API收集的在线学生反馈的新方法。这项工作的结果将有助于学生改善学习，并帮助教师提高他们的教学技能。在这项工作中，已使用分类和聚类技术对意见进行分类。

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