Compare Suite ONLINE

Analyze and compare two articles with Compare Suite ONLINE.

Step 1. Get two articles to analyze with Compare Suite ONLINE.

Step 2. Compare with Compare Suite ONLINE.

Step 3. Analyze the results of comparison.

Step 4. Sending report by e-mail.

General analysis conclusions:

General article idea: "Spam on mobile phones via sms message". It follows from common words statistic.
CNN's article tells about NTT DoCoMo Japan's biggest mobile phone company. It follows from "Unique keywords table - 1".
ZD-Net's article has a comments of Mike Grenville, chief executive of the mobile messaging association. It follows from "Unique keywords table - 2".

Step - 1

Find two articles to analyze with Compare Suite ONLINE:


"Spam invasion targets mobile phones" - CNN CNN's article tells about NTT DoCoMo Japan's biggest mobile phone company. It follows from "Unique keywords table - 1".		"War against spam moves to mobile phones" - ZDNET ZD-Net's article has a comments of Mike Grenville, chief executive of the mobile messaging association. It follows from "Unique keywords table - 2".

Step - 2

Point your web-browser to http://www.comparesuite.com/online.htm

Point your web-browser to http://www.comparesuite.com/online.htm

Log-in into Compare Suite ONLINE and put articles' text into text boxes.

You should select and copy to clipboard article's text.

And then paste into Compare Suite ONLINE text box.

And then paste into Compare Suite ONLINE text box.

Click "Compare" button to start comparison. See the result of comparison: e-mailed comparative report

Step - 3

Analyzing the result of comparison. Common statistic:

Text 1: 3,57 Kb, 582 word(s), 190 unique word(s)
Text 2: 3,55 Kb, 594 word(s), 207 unique word(s)
Common words number: 57
Similarity (by keywords): 12,6%

Note: that similarity rate of two documents is 97,9%. It means that almost all words within documents are common and appears in both - the first and the second text.

Compared texts with highlighted common and unique words:

Compared texts with highlighted common and unique words

Common keywords table:

Common Keywords:

Keyword	Frequency in text 1	Frequency in text 2
spam	10	12
mobile	8	14
messages	14	4
sms	9	6
phone	7	7
users	8	5
said	6	3
receive	5	3
problem	3	4
such	3	3
text	4	2
phones	2	3
message	3	2
become	2	3
companies	1	4
day	3	1
number	1	3
send	2	2
because	2	1
added	2	1
sent	1	2
even	2	1
some	1	2

Unique keywords table:

Unique keywords in text 1:

Keyword	Frequency
docomo	5
like	5
mail	5
where	4
japan	4
people	4
big	3
also	3
average	3
marketers	3
china	3
they	3

Unique keywords in text 2:

Keyword	Frequency
email	7
grenville	5
than	3
industry	3
while	3
services	3
received	3
more	3
silicon	3
much	3

General conclusions

The texts' idea is: "Spam on mobile phones via sms message". It follows from common words statistic.

Keyword	Frequency in text 1	Frequency in text 2
spam	10	12
mobile	8	14
messages	14	4
sms	9	6
phone	7	7

CNN's article also tells about NTT DoCoMo Japan's biggest mobile phone company:

"Mobile phone companies were reluctant to talk about the trend, but evidence of the problem abounded on the Web site of NTT DoCoMo, Japan's biggest mobile phone company"

It follows from unique keywords table - 1:

Unique keywords in text 1:

Keyword	Frequency
docomo	5
like	5
mail	5
where	4
japan	4
people	4

ZD-Net's article has a comments of Mike Grenville, chief executive of the mobile messaging association.

It follows from unique keywords table - 2:

Keyword	Frequency
email	7
grenville	5
than	3
industry	3
while	3
services	3
received	3

Step - 4

Sending report by e-mail

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