Website Classification Using Latent Dirichlet Allocation and its Application for Internet Advertising

被引：0

作者：

Katsumata, Sotaro ^{[1
]}

Motohashi, Eiji ^{[2
]}

Nishimoto, Akihiro ^{[3
]}

Toyosawa, Eiji ^{[4
]}

机构：

[1] Osaka Univ, Graduage Sch Econ, 1-7 Machikaneyama, Toyonaka, Osaka 5650043, Japan

[2] Yokohama Natl Univ, Grad Sch Int Social Sci, Hodogaya Ku, 79-4 Tokiwadai, Yokohama, Kanagawa 2408501, Japan

[3] Kwansei Gakuin Univ, Sch Business Adm, 1-155 Uegahara Ichiban Cho, Nishinomiya, Hyogo 6628501, Japan

[4] F N Commun Inc, Shibuya Ku, Aoyama Diamond Bldg,Recept 2nd Floor, Tokyo 1500002, Japan

来源：

2016 IEEE 16TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW) | 2016年

关键词：

D O I：

10.1109/ICDMW.2016.141

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This study proposes a model for website classification using website content, and discusses applications for internet advertising (ad) strategies. Internet ad agencies have many ad-spaces embedded in many websites and can choose where to place advertisements. Therefore, ad agencies have to know the properties and topics of each website in order to optimize advertising submission strategy. However, since website content is in natural languages, they have to convert these qualitative sentences into quantitative data if they want to classify websites using statistical models. To address this issue, this study applies statistical analysis to website information written in natural languages. We apply a dictionary of neologisms in order to decompose website sentences into words and create a dataset of {0, 1} indicator matrices to classify the websites. From the dataset, we estimate the topics of each website using latent Dirichlet allocation. Finally, we discuss how to apply the results obtained to optimize ad strategies.

引用

页码：538 / 544

页数：7

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