基于社交媒体数据的事件识别研究

Rishov Nag, Soumik De, Nabhoneel Majumdar, Pratik Dutta
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引用次数: 0

摘要

本文解决了没有交通推文分类方法试图识别拥堵原因的事实。我们的目标是对与交通相关的推文进行多类分类,将其分成基于交通拥堵原因的组。我们对数据集执行了各种聚类技术,数据集是我们从加尔各答交通警察的Twitter手柄中获得的。聚类为我们提供了期望的结果,即根据引起拥塞的事件类型将tweet分为四大类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on Event Identification on Social Media Data
This paper addresses the fact that there is no traffic tweet classification methods that try to identify the cause of the congestion. Our goal is to perform a multiclass classification of traffic-related tweets into traffic-congestion-cause-based groups. We perform various clustering techniques on our dataset, which we obtained from the Kolkata Traffic Police's Twitter handle. The clustering gives us the desired result of classifying the tweets into four broad categories based on the type of event causing the congestion.
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