Road Traffic Topic Modeling on Twitter using Latent Dirichlet Allocation

被引：0

作者：

Hidayatullah, Ahmad Fathan ^{[1
]}

Ma'arif, Muhammad Rifqi ^{[2
]}

机构：

[1] UII, Dept Informat, Yogyakarta, Indonesia

[2] STMIK Jend Achmad Yani, Dept Informat Management, Yogyakarta, Indonesia

来源：

2017 INTERNATIONAL CONFERENCE ON SUSTAINABLE INFORMATION ENGINEERING AND TECHNOLOGY (SIET) | 2017年

关键词：

topic modeling; latent dirichlet allocation; Twitter;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Information about road traffic is the one of the most important information for people around the world. TMC (Traffic Management Center) as one of the unit within the Indonesian National Police institution who in charge of traffic management has utilized Twitter as the medium to share about traffic information to the Indonesian citizen. This research aims to create the topic model regarding traffic information on Indonesian Twitter messages. The data used in this research were retrieved from the official Twitter account of the Traffic Management Center in Java using the method of LDA (Latent Dirichlet Allocation) to build the topic model from the dataset. The topic model obtained will represent what kind of topics which posted by TMC in each region in Java. Therefore, the result of this experiment could illustrate valuable and important information that happened in Java Island.

引用

页码：47 / 52

页数：6

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