International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 6, June 2013
ISSN 2319 - 4847
Data Extraction and Web page Categorization
using Text Mining
Lambodar Jena1, Narendra Kumar Kamila2
Department of Computer science & Engineering,
1
Gandhi Engineering College, Bhubaneswar.
2
C.V.Raman college of Engineering, Bhubaneswar.
ABSTRACT
In this paper, we are using boilerplate codes for classifying the individual text elements in a Web page. We enhanced it for date
as well as content extraction from a web page by using text analysis process. Similarly classification of web page content is
essential to many tasks in web information retrieval such as maintaining web directories, to organize the web information as
well as focused crawling. Moreover the algorithm and code has improved for web page categorization. This work is
implemented with twenty to thirty web sites and proven that it is correct which can be enhanced more by using web page
application. With the help of our model, we proved that web pages categorization and date extraction algorithm is 90 % correct.
It categorizes all the web pages and extracts all the relevant dates enclosed within a web page with its relevant content as well as
title. Here we are achieving a remarkable accuracy for categorization of web pages , date extraction, title extraction ,as well as
for comment extraction .
Keywords-- Text mining, Date extraction, Web page categorization.SPO, text analysis
1. INTRODUCTION
When examining a Web page, humans can easily distinguish the main content from navigational text, advertisements,
related articles and other text portions. A number of approaches have been introduced to automate this distinction,
using a combination of heuristic segmentation and features[1]. However, we are not aware of a systematic analysis of
which features are the most salient for extracting those. Similarly classification plays a vital role in many information
management and retrieval tasks. On the Web, classification of page content is essential to focused crawling, to the
assisted development of web directories, to topic-specific web link analysis, and to analysis of the topical structure of
the Web[2]. Web page classification can also help improve the quality of web search. In this paper we analyse the most
popular features used for boilerplate detection on two corpora and extended for web page classification. “Web page
classification, also known as web page categorization, is the process of assigning a web page to one or more predefined
category labels”. Classification is often posed as a supervised learning problem in which a set of labelled data is used to
train a classifier which can be applied to label future examples. So here the web page classification and date extraction
has been done with the help of open source boiler plate[2] text .Boiler plate is an open source text which is coming in
use for the extraction of title as well as relevant content from a web page so it has been extended for extracting the dates
by designing an algorithm. The algorithm is designed in regx patterns and implemented with the help of coding. For
coding purpose java language and natural language processor are used which is discussed here in this paper. The
coding is implemented with twenty to thirty web sites which is 90% correct and it can be extended for more number of
websites by enhancing the web page application as well as the date written patterns in the corresponding algorithm.
Another concepts which is used here is Spring and Ajax for the development of test applications.
1.1 Text mining
Text mining roughly equivalent to text analytics refers to the process of deriving high-quality information from text.
High-quality information is typically derived through the devising of patterns and trends such as statistical pattern
learning. That means deriving patterns within the structured data and evaluation and interpretation of the output. The
term text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure
the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation.
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Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 6, June 2013
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Fig. 1: Text Mining process
1.2 Search process optimization
Process optimization is the discipline of adjusting a process so as to optimize some specified set of parameters without
violating some constraint. The most common goals are minimizing cost, maximizing throughput, and/or efficiency.
This is one of the major quantitative tools in industrial decision making. The goal of search optimization software is to
make sure that the search engines deliver a link based on the various parameters available to search along with the
content a user want to look for using Search.
Search process optimization benefits:
 When optimizing a process, the goal is to maximize one or more of the process specifications, while keeping all
others within their constraints.
 The purpose of Search process optimization is to allow effective search using Information Extraction not using
Information Retrieval on the collection of Unstructured Text.
 Search to use the IE using Text Mining techniques to get the information from the unstructured Text. The
Information Extraction steps allows you to configure your search terms based on more parameters available like search
using dates, and search between news articles, blogs , forum etc. Information Extraction (IE) systems analyze
unrestricted text in order to extract information about pre-specified types of events, entities or relationships.
 Information extraction is all about deriving structured factual information from unstructured text.
 Example, If one want to search where Mr President visited last month, you can’t achieve this , one has to type
specifically Mr President and the country name to get the exact result, so one need to aware of the search terms to get
the result from the IR.
Fig. 2 : Search process optimization steps
2. RELATED WORK
Using a combination of approaches, Gibson et al. quantify the amount of template content in the Web (40%-50%) [15].
Chakrabarti et al. determine the \templateness" of DOM nodes by a classifer based upon regularized isotonic regression
[14] using various DOM-level based features, including shallow text features as well as site-level hyperlink
information. Template detection is strongly related to the more generic problem of web page segmentation, which has
been addressed at DOM-level by exploiting term entropies [17] or by using Vision-based features. Similarly, in
(Kohlschütter et. al., 2010) decision trees are applied on shallow text features in order to classify the individual text
Volume 2, Issue 6, June 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
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Volume 2, Issue 6, June 2013
ISSN 2319 - 4847
elements of an HTML page. Other approaches try to exploit structural information, mainly contained in the DOM
(Document Object Model) tree of an HTML page, in order to identify common, i.e. frequently used patterns or
segments, usually concentrated on specific web sites. (Chakrabarti et. al., 2007) try to determine the “templateness” of
DOM nodes, by training Logistic regression classifiers over a set o features, such as closeness to the margins of an
HTML page, number of links per word, fraction of text within anchors, size of anchors, etc. At a second level,
smoothing is applied on the classifier scores of DOM nodes using regularised isotonic regression to improve
performance.
3. RELATED STUDIES
A small set of shallow text features and web page features have been analysed for classifying the individual text
elements in a Web page[2]. Moreover, this paper derive a simple and plausible stochastic model for describing the
boilerplate creation process and the accuracy graph has been shown by taking Google news website with the help of
link density , text density, number of words. The topic of cleaning arbitrary web pages with the goal of extracting a
corpus from web data, which is suitable for linguistic and language technology research and development, has attracted
significant research interest recently[4]. Several general purpose approaches for removing boilerplate have been
presented in that literature .it provides the finer control over the extracted textual segments in order to accurately
identify important elements, i.e. individual blog posts, titles, posting dates or comments. BlogBuster tries to provide
such additional details along with boilerplate removal, following a rule-based approach. A small set of rules were
manually constructed by observing a limited set of blogs from the Blogger and Wordpress hosting platforms. These
rules operate on the DOM tree of an HTML page, as constructed by a popular browser, Mozilla Firefox[19] Chekuri et
al. studied automatic web page classification in order to increase the precision of web search. A statistical classifier,
trained on existing web directories, is applied to new web pages and produces an ordered list of categories in which the
web page could be placed. here the classification category have been studied in the different point of view like binary
,multiclass, flat, hierarchical, soft and hard classification.
4. PROBLEM STATEMENT
Boilerplate is an open source text Frame work which is in the reference paper using for the tittle extraction and
effective content extraction (the corresponding graph have been shown here ) but it does not work for the dates enclosed
within a web page .Similarly for web page categorization the algorithms does not work.
Fig. 3:Text density distribution by class for Google news
In the above graph text Density distribution[2] has been shown with respect to the number of words the distribution
function has been shown in form of class for main content , related content and headlines but not for the relevant dates .
In this paper we are working for relevant dates as well as work is extended more for categorization.
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Volume 2, Issue 6, June 2013
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5. DATE RETRIEVAL
Web page contains the list of items like title, comment, main content and date. since date is also an important content
in a web page but by using boiler plate open source we can extract only the title and main content but not the date so it
is extended for date extraction .Dates on a web pages may be present in the different formats so it can be extracted by
using date allow filter on boiler plate codes. To extract the date we have used the natural language processor and
written it into fixed pattern. With the simulation it has been proved that date extraction algorithm is 90% correct.
Documents contain dates explicitly stated in the places like URL, Title, Body of the document, meta tags of the
document and Last-modified date from the HTTP response.
The Algorithm extracts all the dates for a document with a matching URL pattern that fits the date format associated
with the rule. If a date is written in an ambiguous format, the algorithm assumes that it matches the most common
format among URLs that match each rule for each domain that is collected. For this purpose, a domain is one level
above the top level. For example, mycompany.com is a domain, but intranet.mycompany.com is not a domain. The
algorithm runs a process that calculates which of the supported date formats is the most common for a rule and a
domain. After calculating the statistics for each pattern, the process may modify the dates in the data.
Here we work on the Algorithm to extract different dates format present in the body of the content.
5.1 Date retrieval algorithm
In this algorithm the array of months are taken .MM is the numeric month format which is written where square
bracket is the optional bracket similarly date patterns DD are written and yyyy/yy is the year format where d stands
for digits from 1 to 9. The separator may be dot((.) slash(/) ,or hyphen(-) which is taken as string.
months[]={"january","february","march","april","may","june","july","august",
"september","october","november","december"};
MM=(0[1-9]|1[012]);
DD=(0[1-9]|[12][0-9]|3[01]);
YYYY=([19|20]\\d\\d);
YY=("\\d\\d");
string [-/.]
int numwords;
char/int=month, int MM, int DD, int YYYY/YY var x;
{if((x=="today")||(x=="tomorrow"))
{if((x=="post")||x.length==9||x.length==5)
if(numwords<=4)
{return x;} }
else if(x=MM[-/.]DD[-/.]YYYY)
{
Step i) compile this pattern with the pattern object
Step ii) match this pattern with the matcher object
Step iii)return the matched value in solar date format .
Step iv) return x; }
else if(x=MM[-/.]DD[-/.]YY)
{ go for step i to iv; }
else if(x=DD[-/.]MM[-/.]YYYY)
{ go for step i to iv; }
else if(x=DD[-/.]MM[-/.]YY)
{ go for step I to iv; }
else if(x=YYYY[-/.]DD[-/.]MM)
{ go for step i to iv; }
else if(x=DD[-/.]MMM[-/.]YYY)
{ go for step i , ii, iii ,iv; }
else if(x=MMM[-/.]DD[-/.]YYY)
{ go for step i , ii, iii ,iv; }
Volume 2, Issue 6, June 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 6, June 2013
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Fig.4:Date Extraction with data cleaning
5.2 Date retrieval pattern samples
Date Format : yyyy-MM-dd
[0-9]{4})[-](0?[1-9]|1[012])[-]([12][0-9]|3[01]|0?[1-9])
Date Format : yyyy-dd-MM
[0-9]{4})[-](0?[1-9]|[12][0-9]|3[01])[-](1[012]|0?[1-9])
Date Format : MM-dd-yyyy
1[012]|0[1-9])[-](0?[1-9]|[12][0-9]|3[01])[-]([0-9]{4})"), "MM-dd-yyyy")
Date Format : dd-MM-yyyy
0[1-9]|[12][0-9]|3[01])[-](0?[1-9]|1[012])[-]([0-9]{4})
Date Format : MM-dd-yyyy
[1-9])[-](0?[1-9]|[12][0-9]|3[01])[-]([0-9]{4}
Date Format : dd-MM-yyyy
[1-9])[-](0?[1-9]|1[012])[-]([0-9]{4}
6. PAGE CLASSIFICATION
Classification plays a vital role in many information management and retrieval tasks. On the Web, classification of
page content is essential to focused crawling, to the assisted development of web directories, to topic-specific web link
analysis, and to analysis of the topical structure of the Web. Web page classification can also help improve the quality
of web search. “Web page classification, also known as web page categorization, is the process of assigning a web page
to one or more predefined category labels”. Classification is often posed as a supervised learning problem in which a set
of labeled data is used to train a classifier which can be applied to label future examples The general problem of web
page classification can be divided into multiple sub-problems: subject classification, functional classification, sentiment
classification, and other types of classification. Subject classification is concerned about the subject or topic of a web
page. For example, judging whether a page is about “arts”, “business” or “sports” is an instance of subject
Volume 2, Issue 6, June 2013
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classification. Functional classification cares about the role that the web page plays. For example, deciding a page to be
a “personal homepage”, “course page” or “admission page” is an instance of functional classification. Sentiment
classification focuses on the opinion that is presented in a web page, i.e., the author’s attitude about some particular
topic. Other types of classification include genre classification ,search engine spam classification and so on…..Based on
the number of classes in the problem, classification can be divided into binary classification and multi-class
classification, where binary classification categorizes instances into exactly one of two classes multi-class classification
deals with more than two classes. Based on the organization of categories, web page classification can also be divided
into flat classification and hierarchical classification. In flat classification, categories are considered parallel, i.e., one
category does not supersede another. While in hierarchical classification, the categories are organized in a hierarchical
tree-like structure, in which each category may have a number of subcategories.
6.1 Page categorization algorithm building blocks
Fig.5: Page Categorization
6.2 Algorithm description
A. Input the system with HTML Text received from the data cleaning
B. Check for the category Matching
Check for the URL
a. Match the URL for predefined words for blog, forum, News
b. If Matched Mark the content with the matched word category set
c. Exit
d. Check for the Metadata
e. Identify the content in the Meta data Tag
f. Match the Meta tag for predefined words for blog, forum, News
g. If Matched Mark the content with the matched word category set
h. Exit
i. Check for the Menu
j. Identify the content in the Menu Tag consisting <li>
k. Match the Menu tag for predefined words for blog, forum, News
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l. If Matched Mark the content with the matched word category set
m. Exit
n. Check for the User Input
o. Identify the User Input Section
p. Input sections have generally comment, feedback reply sections
q. Match the Input Section for predefined words for blog, forum, News
r. If Matched Mark the content with the matched word category set
s. Exit
7. SIMULATION OUTPUTS
The software tools JAVA, Spring Framework , GATE NLP and OPEN NLP are used to simulate our work for date
retrieval and page categorization.
Date Retrieval:
Simulation is carried on Date Retrieval Algorithm based on the test data for 15 selected random URLs to identify the
correctness of Date Retrieval Algorithm.
Table-1
URLS
Output
http://www.indianexpress.com/news/from-worlds-apart-siblings-aretracing-roots-of-schizophrenia/1070518/
Success
http://www.indianexpress.com/news/it-official-goes-to-hc-againstfms-decision-to-allow-his-prosecution/1070515/
Success
http://www.indianexpress.com/news/guv-said-no--yr-later-4-andhracongmen-get-info-body-posts/1070509/
Success
http://javarevisited.blogspot.in/2011/12/difference-between-dom-andsax-parsers.html
Success
http://javarevisited.blogspot.in/2011/12/difference-between-dom-andsax-parsers.html
Success
http://thenextweb.com/insider/2013/02/07/twitters-biz-stone-makesdirectorial-debut-with-evermore-as-part-of-canons-projectimaginat10n/
http://javarevisited.blogspot.in/2013/02/disable-submit-button-inhtml-javascript-avoid-multiple-form-submission.html
Success
http://timesofindia.indiatimes.com/india/In-12-hours-Mumbai-ladyscredit-card-used-in-4-continents/articleshow/18374084.cms
Success
http://www.thehindu.com/news/national/armys-stand-makes-it-hardto-amend-afspa-chidambaram/article4386754.ece?homepage=true
Success
http://www.indiatimes.com/
Failure
Success
Date Retrieval…
Success
Failure
Fig.6: Date retrieval
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Page Classification:
Simulation is carried on Page Classification Algorithm based on the test data for 15 selected random URLs to identify
the correctness of Page Classification Algorithm.
Table-2
URLS
Output
http://www.indianexpress.com/news/from-worlds-
Success
apart-siblings-are-tracing-roots-ofschizophrenia/1070518/
http://www.indianexpress.com/news/it-official-
Success
goes-to-hc-against-fms-decision-to-allow-hisprosecution/1070515/
http://www.indianexpress.com/news/guv-said-no--
Success
yr-later-4-andhra-congmen-get-info-bodyposts/1070509/
http://javarevisited.blogspot.in/2011/12/difference
Success
-between-dom-and-sax-parsers.html
http://javarevisited.blogspot.in/2011/12/difference
Success
-between-dom-and-sax-parsers.html
http://thenextweb.com/insider/2013/02/07/twitters
Success
-biz-stone-makes-directorial-debut-withevermore-as-part-of-canons-project-imaginat10n/
http://javarevisited.blogspot.in/2013/02/disable-
Success
submit-button-in-html-javascript-avoid-multipleform-submission.html
http://timesofindia.indiatimes.com/india/In-12-
Success
hours-Mumbai-ladys-credit-card-used-in-4continents/articleshow/18374084.cms
http://www.thehindu.com/news/national/armys-
Success
stand-makes-it-hard-to-amend-afspachidambaram/article4386754.ece?homepage=true
http://www.indiatimes.com/
Volume 2, Issue 6, June 2013
Failure
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Fig.7: Page classification
8. CONCLUSION
Text mining is a burgeoning technology, because of its newness and intrinsic difficulty, in a fluid state, perhaps, to the
state of machine learning. When the term is broadly interpreted, many different problems and techniques come under
its ambit. In most cases it is difficult to provide general and meaningful evaluations because the task is highly sensitive
to the particular text under consideration. Document classification, entity extraction like date , and filling templates
that correspond to given relationships between entities, are all central text mining operations that have been extensively
studied. Using structured data such as Web pages rather than plain text as the input opens up new possibilities for
extracting information from individual pages and large networks of pages. Automatic text mining techniques have a
long way to go before they rival the ability of people, even without any special domain knowledge, to glean information
from large document collections. This work extended further to extract the relevant information and filtering out the
comment section with the sentiments i.e whether the comment is positive or negative. the performance graph can be
extended by defining codes for number of date patterns i.e design the code for all the predefined date formats. The
future work may also be extended for the specific content search. Webpage classification can help improve the quality
of web search and improve SEO skill.
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AUTHOR
Lambodar Jena received his M.Tech degree in Computer Science and Application from Indian School of Mines(ISM),
Dhanbad in the year 2000 and currently pursuing doctorate(Ph.D) degree from Utkal University, India . His research
interest includes data mining ,data privacy, image mining, web mining and wireless sensor networking. He has several
publications in national/international journals and conference proceedings.
Narendra Kumar Kamila received his M.Tech degree in Computer Science and Engineering from Indian Institute of
Technology, Kharagpur and doctorate(Ph.D) degree from Utkal University, India in the year 2000. Later he had visited
USA for his post doctoral work at University of Arkansas in 2005. His research interest includes artificial intelligence,
data privacy, image processing and wireless sensor networking. He has several publications in journal and conference
proceedings. His professional activities include teaching computer science, besides he organizes many conferences, workshops and
faculty development programs funded by All India Council for Technical Education, Govt. of India. Dr. Kamila has been rendering
his best services as editorial board member and editor-in-chief of many international journal and conference proceedings.
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