research question

What are the words frequency of these websites?

KEYWORDS CHARITABLEORGANIZATION COMPANYWEB DATA CENTERWEB FORUMWEB GOVERNMENT NEWSMEDIA REFERENCE KEYWORDS CHARITABLEORGANIZATION COMPANYWEB DATA CENTERWEB FORUMWEB GOVERNMENT NEWSMEDIA REFERENCE SIZE COLOR WORDS FRENQUENCY ARTICLE SERVICE NEWS REPORT PAPER TUTORIAL LEGEND POWER CONSUMPTION USE TECHNOOGY SOLVE COMPUTE PROGRAM CLOUD SYSTEM POWER COMPANIES SERVICE PRODUCT MANAGEMENT NOMAL GUANGZHOU INTERNET INDUSTRY DIGIAL APPLICATION CONFERENCE BATTERY DISTRIBUTED CLOUD SYSTEM POWER SERVER COOLING CONSUMPTION EFFICIENCY 2018 MANAGEMENT WATER ENERGY SAVING SECURITY BIG REPORT ENTERPRISE MOBILE SYSTEM SERVER COMPANIES COST EFFICIENCY BUILDING STATES HOME FEDERAL COMPANIES EFFICIENCY RESOURCE MEMBERSHIP FORTUNE INCREASE CRITICAL COOLING USE COOKIES IEEE CERTAIN WEBSITE SIGN ARTICLES PASSWORD HISTORY 6 8 14 5 3 9 6 12 6 3 5 13 10 9 9 20 12 33 25 13 6 21 20 87 11 25 51 51 63 47 88 50 30 12 17 9 30 9 15 30 104 75 25 25 15 50 8 38 14 37 85 44 22 175 175 217 173 4 46 75 28 30 19 26 20 17 14 13 5 91 91 11 27 34 31 7 12 33
Description Protocol Data

Description

To further explore the specific content of the 50 links, we analyze and conclude: Industry technology companies (such as Google Amazon), they are more concerned about data center applications (Solve, Service, Product, Cloud, Program) and stability (System, Power). Professional websites pay more attention to: system(cooling),application (such as cloud) and energy (Consumption, Energy Saving). As for the government aspect (the results are all from Google), the US government’s focus on data centers are energy efficiency, cost, and construction.

In summary, we can conclude that the top 25 webpages(baidu or google)obtained by search engines are more suitable for decision makers in professional fields, industry organizations and decision makers at relevant governments.

Protocol

example of protocol

We used the 50 urls in the first protocol, and then 50 urls were input into VOYANT-TOOLS. We got some keywords and their frequency. At the same time, the searched text and irrelevant text were deleted. Finally, we sorted the words by frequency and divided them into 6 categories according to the content.

Data

Data Source: Google , Baidu
Timestamp: 02/11/2018
View Data (44Kb)

Our dataset was consisted of a table containing both Chinese and English search results, including the frequency of keywords, the type of website and content.