Volume 18, No. 2, 2021
Automatic Web Page Classification System with Improved Accuracy
Chaithra, Dr.G.M. Lingaraju and Dr.S. Jagannatha
Abstract
Nowadays, the Internet contain s a wide variety of online documents, making finding useful information about a given subject impossible, as well as retrieving irrelevant pages. Web document and page recognition software is useful in a variety of fields, including news, medicine, and fitness, research, and information technology. To enhance search capability, a large number of web page classification methods have been proposed, especially for news web pages. Furthermore existing classification approaches seek to distinguish news web pages while still reducing the high dimensionality of features derived from these pages. Due to the lack of automated classification methods, this paper focuses on the classification of news web pages based on their scarcity and importance. This work will establish different models for the identification and classification of the web pages. The data sets used in this paper were collected from popular news websites. In the research work we have used BBC dataset that has five predefined categories. Initially the input source can be preprocessed and the errors can be eliminated. Then the features can be extracted depend upon the web page reviews using Term frequency-inverse document frequency vectorization. In the work 2225 documents are represented with the 15286 features, which represents the tf-idf score for different unigrams and bigrams. This type of the representation is not only used for classification task also helpful to analyze the dataset. Feature selection is done by using the chi-squared test which will be in the task of finding the terms that are most correlated with each of the categories. Then the pointed features can be selected using chi-squared test. Finally depend upon the classifier the web page can be classified. The results showed that list has obtained the highest percentage, which reflect its effectiveness on the classification of web pages.
Pages: 225-242
DOI: 10.14704/WEB/V18I2/WEB18318
Keywords: Web Page Classification, Term Frequency-inverse Document Frequency.