Categorical Data Analysis and Pattern Mining of Top Colleges in India by Using Twitter Data

Nehal Mamgain, B. Pant, A. Mittal
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引用次数: 5

Abstract

This paper is a detailed summary of the work conducted in the novel domain of categorical data analysis of eminent colleges in India by mining Twitter data and uncovering integral traits/events characteristic of these institutes by determining key rules. The information thus collected could be beneficial to the entire academia: it can be utilized by students in making informed decisions about which college to join or by institutes themselves to address their potentially weak points and maintain the standards of their positive features. Apart from performing extensive preprocessing including spelling correction and netspeak expansion, irrelevant tweets were further segregated by means of a unigram dictionary containing education-oriented keywords. The Apriori algorithm was then applied to the dataset thus obtained resulting in characteristic markers or patterns of these institutes.
基于Twitter数据的印度顶尖大学分类数据分析与模式挖掘
本文通过挖掘Twitter数据,并通过确定关键规则揭示这些机构的整体特征/事件特征,详细总结了在印度著名大学分类数据分析的新领域开展的工作。这样收集到的信息对整个学术界都是有益的:学生可以利用这些信息来做出明智的决定,决定加入哪所大学,或者由学院自己来解决他们潜在的弱点,保持他们积极特征的标准。除了进行大量的预处理,包括拼写纠正和网络语言扩展,不相关的推文被进一步分离,通过一个包含教育导向关键字的一元字典。然后将Apriori算法应用于数据集,从而获得这些研究所的特征标记或模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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