A Machine Learning-Based Model to Evaluate Readability and Assess Grade Level for the Web Pages

2020 ◽  
Author(s):  
Muralidhar Pantula ◽  
K S Kuppusamy
Keyword(s):  
Machine Learning ◽  
Grade Level ◽  
Web Pages ◽  
Word Count ◽  
Web Page ◽  
Web Documents ◽  
Browser Extension ◽  
Statistical Measures ◽  
Proposed Model ◽  
The Web

Abstract Evaluating readability of web documents has gained attention due to several factors such as improving the effectiveness of writing and to reach a wider spectrum of audience. Current practices in this direction follow several statistical measures in evaluating readability of the document. In this paper, we have proposed a machine learning-based model to compute readability of web pages. The minimum educational standards required (grade level) to understand the contents of a web page are also computed. The proposed model classifies the web pages into highly readable, readable or less readable using specified feature set. To classify a web page with the aforementioned categories, we have incorporated the features such as sentence count, word count, syllable count, type-token ratio and lexical ambiguity. To increase the usability of the proposed model, we have developed an accessible browser extension to perform the assessments of every web page loaded into the browser.

scholarly journals Improving Performance of DOM in Semi-structured Data Extraction using WEIDJ Model

2018 ◽  
Vol 9 (3) ◽  
pp. 752 ◽  
Author(s):  
Ily Amalina Ahmad Sabri ◽  
Mustafa Man
Keyword(s):  
Data Extraction ◽  
Extraction Process ◽  
Structured Data ◽  
Web Pages ◽  
Web Page ◽  
Web Data ◽  
Web Documents ◽  
Web Extraction ◽  
Comparison Time ◽  
The Web

<p>Web data extraction is the process of extracting user required information from web page. The information consists of semi-structured data not in structured format. The extraction data involves the web documents in html format. Nowadays, most people uses web data extractors because the extraction involve large information which makes the process of manual information extraction takes time and complicated. We present in this paper WEIDJ approach to extract images from the web, whose goal is to harvest images as object from template-based html pages. The WEIDJ (Web Extraction Image using DOM (Document Object Model) and JSON (JavaScript Object Notation)) applies DOM theory in order to build the structure and JSON as environment of programming. The extraction process leverages both the input of web address and the structure of extraction. Then, WEIDJ splits DOM tree into small subtrees and applies searching algorithm by visual blocks for each web page to find images. Our approach focus on three level of extraction; single web page, multiple web page and the whole web page. Extensive experiments on several biodiversity web pages has been done to show the comparison time performance between image extraction using DOM, JSON and WEIDJ for single web page. The experimental results advocate via our model, WEIDJ image extraction can be done fast and effectively.</p>

scholarly journals Semantic Features with Contextual Knowledge-Based Web Page Categorization Using the GloVe Model and Stacked BiLSTM

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1772
Author(s):  
Amit Kumar Nandanwar ◽  
Jaytrilok Choudhary
Keyword(s):  
Short Term Memory ◽  
Web Pages ◽  
Semantic Features ◽  
Contextual Knowledge ◽  
Web Page ◽  
Short Term ◽  
Knowledge Based ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
The Web

Internet technologies are emerging very fast nowadays, due to which web pages are generated exponentially. Web page categorization is required for searching and exploring relevant web pages based on users’ queries and is a tedious task. The majority of web page categorization techniques ignore semantic features and the contextual knowledge of the web page. This paper proposes a web page categorization method that categorizes web pages based on semantic features and contextual knowledge. Initially, the GloVe model is applied to capture the semantic features of the web pages. Thereafter, a Stacked Bidirectional long short-term memory (BiLSTM) with symmetric structure is applied to extract the contextual and latent symmetry information from the semantic features for web page categorization. The performance of the proposed model has been evaluated on the publicly available WebKB dataset. The proposed model shows superiority over the existing state-of-the-art machine learning and deep learning methods.

Survey on Optimization Algorithms Used for Feature Selection Techniques in Web Page Classification

2019 ◽  
Vol 16 (2) ◽  
pp. 384-388 ◽  
Author(s):  
K. S. Ramanujam ◽  
K. David
Keyword(s):  
Machine Learning ◽  
Web Mining ◽  
Research Area ◽  
Web Pages ◽  
Web Page ◽  
Web Page Classification ◽  
Significant Research ◽  
Multiple Classifier ◽  
The Web ◽  
Page Classification

Web page classification refers to one of the significant research are in the web mining domain. Enormous quantity of data existing in the web demands the essential development of various effective and robust techniques to undergo web mining task that involves the process to categorizing the web page based on the data labels. It also includes various other tasks such as web crawling, analysis of web links and contextual advertising process. Existing machine learning and data mining techniques are being efficiently used for various web mining processes which include classification of web pages. Using of multiple classifier techniques are most promising research area while considering machine learning that works on the base of merging various classifiers with difference in base classifier and/or dataset distribution. With this several classification models are constructed that is highly robust in nature. This review paper, comparison has been done between FA, PSO, ACO, GA and IWT, to evaluate best fit algorithm for classifying web pages.

IMPROVED WEB PAGE IDENTIFICATION METHOD USING NEURAL NETWORKS

2011 ◽  
Vol 10 (01) ◽  
pp. 87-114 ◽  
Author(s):  
ALI SELAMAT ◽  
ZHI SAM LEE ◽  
MOHD AIZAINI MAAROF ◽  
SITI MARIYAM SHAMSUDDIN
Keyword(s):  
Neural Networks ◽  
Feature Selection ◽  
Weighting Scheme ◽  
Web Pages ◽  
Web Page ◽  
Term Weighting ◽  
Web Documents ◽  
Web Contents ◽  
Feature Selection Approach ◽  
The Web

In this paper, an improved web page classification method (IWPCM) using neural networks to identify the illicit contents of web pages is proposed. The proposed IWPCM approach is based on the improvement of feature selection of the web pages using class based feature vectors (CPBF). The CPBF feature selection approach has been calculated by considering the important term's weight for illicit web documents and reduce the dependency of the less important term's weight for normal web documents. The IWPCM approach has been examined using the modified term-weighting scheme by comparing it with several traditional term-weighting schemes for non-illicit and illicit web contents available from the web. The precision, recall, and F1 measures have been used to evaluate the effectiveness of the proposed IWPCM approach. The experimental results have shown that the proposed improved term-weighting scheme has been able to identify the non-illicit and illicit web contents available from the experimental datasets.

Techniques for Refreshing Images in Web Documents

2011 ◽  
Vol 403-408 ◽  
pp. 1008-1013 ◽  
Author(s):  
Divya Ragatha Venkata ◽  
Deepika Kulshreshtha
Keyword(s):  
Search Engine ◽  
Major Part ◽  
Web Pages ◽  
End User ◽  
Web Page ◽  
Web Documents ◽  
Client Server ◽  
Web Document ◽  
Server Architecture ◽  
The Web

In this paper, we put forward a technique for keeping web pages up-to-date, later used by search engine to serve the end user queries. A major part of the Web is dynamic and hence, a need arises to constantly update the changed web documents in search engine’s repository. In this paper we used the client-server architecture for crawling the web and propose a technique for detecting changes in web page based on the content of the images present if any in web documents. Once it is being identified that the image embedded in the web document is changed then the previous copy of the web document present in the search engine’s database/repository is replaced with the changed one.

A FRAME WORK FOR WEB INFORMATION EXTRACTION AND ANALYSIS

2013 ◽  
Vol 7 (2) ◽  
pp. 574-579 ◽  
Author(s):  
Dr Sunitha Abburu ◽  
G. Suresh Babu
Keyword(s):  
Information Extraction ◽  
Data Extraction ◽  
Research Work ◽  
Web Pages ◽  
Web Documents ◽  
E Learning ◽  
Structured Information ◽  
Frame Work ◽  
Effective Decision ◽  
The Web

Day by day the volume of information availability in the web is growing significantly. There are several data structures for information available in the web such as structured, semi-structured and unstructured. Majority of information in the web is presented in web pages. The information presented in web pages is semi-structured.  But the information required for a context are scattered in different web documents. It is difficult to analyze the large volumes of semi-structured information presented in the web pages and to make decisions based on the analysis. The current research work proposed a frame work for a system that extracts information from various sources and prepares reports based on the knowledge built from the analysis. This simplifies  data extraction, data consolidation, data analysis and decision making based on the information presented in the web pages.The proposed frame work integrates web crawling, information extraction and data mining technologies for better information analysis that helps in effective decision making.   It enables people and organizations to extract information from various sourses of web and to make an effective analysis on the extracted data for effective decision making.  The proposed frame work is applicable for any application domain. Manufacturing,sales,tourisum,e-learning are various application to menction few.The frame work is implemetnted and tested for the effectiveness of the proposed system and the results are promising.

Semi-Automatic Online Tagging with K-Medoid Clustering

2014 ◽  
Vol 24 (08) ◽  
pp. 1115-1130 ◽  
Author(s):  
He Hu ◽  
Xiaoyong Du
Keyword(s):  
Clustering Algorithm ◽  
Prototype System ◽  
Web Pages ◽  
Web Page ◽  
Web Browser ◽  
User Input ◽  
Browser Extension ◽  
Annotation Process ◽  
Efficiency And Effectiveness ◽  
Automatic Mechanism

Online tagging is crucial for the acquisition and organization of web knowledge. We present TYG (Tag-as-You-Go) in this paper, a web browser extension for online tagging of personal knowledge on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page annotation. The annotation process supports user-defined tagging schema and comprises an automatic mechanism that is built upon clustering techniques, which can automatically group similar HTML DOM nodes into clusters corresponding to the user specification. TYG is a prototype system illustrating the proposed approach. Experiments with TYG show that our approach can achieve both efficiency and effectiveness in real world annotation scenarios.

Exploring the customer orientation of Spanish pharmacy websites

2018 ◽  
Vol 12 (4) ◽  
pp. 447-462 ◽  
Author(s):  
Carmen Domínguez-Falcón ◽  
Domingo Verano-Tacoronte ◽  
Marta Suárez-Fuentes
Keyword(s):  
Web 2.0 ◽  
Customer Orientation ◽  
Community Pharmacies ◽  
Web Pages ◽  
Web Page ◽  
Pharmaceutical Sector ◽  
Content Type ◽  
Web Page Design ◽  
Page Design ◽  
The Web

Purpose The strong regulation of the Spanish pharmaceutical sector encourages pharmacies to modify their business model, giving the customer a more relevant role by integrating 2.0 tools. However, the study of the implementation of these tools is still quite limited, especially in terms of a customer-oriented web page design. This paper aims to analyze the online presence of Spanish community pharmacies by studying the profile of their web pages to classify them by their degree of customer orientation. Design/methodology/approach In total, 710 community pharmacies were analyzed, of which 160 had Web pages. Using items drawn from the literature, content analysis was performed to evaluate the presence of these items on the web pages. Then, after analyzing the scores on the items, a cluster analysis was conducted to classify the pharmacies according to the degree of development of their online customer orientation strategy. Findings The number of pharmacies with a web page is quite low. The development of these websites is limited, and they have a more informational than relational role. The statistical analysis allows to classify the pharmacies in four groups according to their level of development Practical implications Pharmacists should make incremental use of their websites to facilitate real two-way communication with customers and other stakeholders to maintain a relationship with them by having incorporated the Web 2.0 and social media (SM) platforms. Originality/value This study analyses, from a marketing perspective, the degree of Web 2.0 adoption and the characteristics of the websites, in terms of aiding communication and interaction with customers in the Spanish pharmaceutical sector.

scholarly journals A Web Page Clustering Method Based on Formal Concept Analysis

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 228 ◽  
Author(s):  
Zuping Zhang ◽  
Jing Zhao ◽  
Xiping Yan
Keyword(s):  
Formal Concept Analysis ◽  
Concept Lattice ◽  
Concept Analysis ◽  
Formal Context ◽  
Formal Concept ◽  
Web Pages ◽  
Web Page ◽  
Data Links ◽  
Web Page Clustering ◽  
The Web

Web page clustering is an important technology for sorting network resources. By extraction and clustering based on the similarity of the Web page, a large amount of information on a Web page can be organized effectively. In this paper, after describing the extraction of Web feature words, calculation methods for the weighting of feature words are studied deeply. Taking Web pages as objects and Web feature words as attributes, a formal context is constructed for using formal concept analysis. An algorithm for constructing a concept lattice based on cross data links was proposed and was successfully applied. This method can be used to cluster the Web pages using the concept lattice hierarchy. Experimental results indicate that the proposed algorithm is better than previous competitors with regard to time consumption and the clustering effect.

scholarly journals PREDICTION OF DESIGN ASPECTS OF WEB PAGE BY HTML PARSER

2020 ◽  
Vol 5 (2) ◽  
pp. 143-158
Author(s):  
Satinder Kaur ◽  
Sunil Gupta
Keyword(s):  
User Interaction ◽  
Web Pages ◽  
Evaluation Tool ◽  
Web Page ◽  
Design Quality ◽  
Automated Evaluation ◽  
The World ◽  
Before And After ◽  
The Web

Inform plays a very important role in life and nowadays, the world largely depends on the World Wide Web to obtain any information. Web comprises of a lot of websites of every discipline, whereas websites consists of web pages which are interlinked with each other with the help of hyperlinks. The success of a website largely depends on the design aspects of the web pages. Researchers have done a lot of work to appraise the web pages quantitatively. Keeping in mind the importance of the design aspects of a web page, this paper aims at the design of an automated evaluation tool which evaluate the aspects for any web page. The tool takes the HTML code of the web page as input, and then it extracts and checks the HTML tags for the uniformity. The tool comprises of normalized modules which quantify the measures of design aspects. For realization, the tool has been applied on four web pages of distinct sites and design aspects have been reported for comparison. The tool will have various advantages for web developers who can predict the design quality of web pages and enhance it before and after implementation of website without user interaction.

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