%0 Journal Article %1 Hashemi2020 %A Hashemi, Mahdi %D 2020 %J Multimedia Tools and Applications %K classification page survey web %N 17 %P 11921--11945 %R 10.1007/s11042-019-08373-8 %T Web page classification: a survey of perspectives, gaps, and future directions %U https://doi.org/10.1007/s11042-019-08373-8 %V 79 %X The explosive growth of the amount of information on Internet has made Web page classification essential for Web information management, retrieval, and integration, Web page indexing, topic-specific Web crawling, topic-specific information extraction models, advertisement removal, filtering out unwanted, futile, or harmful contents, and parental control systems. Owing to the recent staggering growth of performance and memory space in computing machines, along with specialization of machine learning models for text and image classification, many researchers have begun to target the Web page classification problem. Yet, automatic Web page classification remains at its early stages because of its complexity, diversity of Web pages' contents (images of different sizes, text, hyperlinks, etc.), and its computational cost. This paper not only surveys the proposed methodologies in the literature, but also traces their evolution and portrays different perspectives toward this problem. Our study investigates the following: (a) metadata and contextual information surrounding the terms are mostly ignored in textual content classification, (b) the structure and distribution of text in HTML tags and hyperlinks are understudied in textual content classification, (c) measuring the effectives of features in distinguishing among Web page classes or measuring the contribution of each feature in the classification accuracy is a prominent research gap, (d) image classification methods rely heavily on computationally intensive and problem-specific analyses for feature extraction, (e) semi-supervised learning is understudied, despite its importance in Web page classification because of the massive amount of unlabeled Web pages and the high cost of labeling, (f) deep learning, convolutional and recurrent networks, and reinforcement learning remain underexplored but intriguing for Web page classification, and last but not least (g) developing a detailed testbed along with evaluation metrics and establishing standard benchmarks remain a gap in assessing Web page classifiers.

@article{Hashemi2020, abstract = {The explosive growth of the amount of information on Internet has made Web page classification essential for Web information management, retrieval, and integration, Web page indexing, topic-specific Web crawling, topic-specific information extraction models, advertisement removal, filtering out unwanted, futile, or harmful contents, and parental control systems. Owing to the recent staggering growth of performance and memory space in computing machines, along with specialization of machine learning models for text and image classification, many researchers have begun to target the Web page classification problem. Yet, automatic Web page classification remains at its early stages because of its complexity, diversity of Web pages' contents (images of different sizes, text, hyperlinks, etc.), and its computational cost. This paper not only surveys the proposed methodologies in the literature, but also traces their evolution and portrays different perspectives toward this problem. Our study investigates the following: (a) metadata and contextual information surrounding the terms are mostly ignored in textual content classification, (b) the structure and distribution of text in HTML tags and hyperlinks are understudied in textual content classification, (c) measuring the effectives of features in distinguishing among Web page classes or measuring the contribution of each feature in the classification accuracy is a prominent research gap, (d) image classification methods rely heavily on computationally intensive and problem-specific analyses for feature extraction, (e) semi-supervised learning is understudied, despite its importance in Web page classification because of the massive amount of unlabeled Web pages and the high cost of labeling, (f) deep learning, convolutional and recurrent networks, and reinforcement learning remain underexplored but intriguing for Web page classification, and last but not least (g) developing a detailed testbed along with evaluation metrics and establishing standard benchmarks remain a gap in assessing Web page classifiers.}, added-at = {2021-01-20T15:49:04.000+0100}, author = {Hashemi, Mahdi}, biburl = {https://www.bibsonomy.org/bibtex/29b3cb4d7f4eb8e27732ea23de22a2d89/parismic}, day = 01, description = {Web page classification: a survey of perspectives, gaps, and future directions | SpringerLink}, doi = {10.1007/s11042-019-08373-8}, interhash = {ddb93c8d731604826930b66f0ee2a1e3}, intrahash = {9b3cb4d7f4eb8e27732ea23de22a2d89}, issn = {1573-7721}, journal = {Multimedia Tools and Applications}, keywords = {classification page survey web}, month = may, number = 17, pages = {11921--11945}, timestamp = {2021-01-20T15:49:04.000+0100}, title = {Web page classification: a survey of perspectives, gaps, and future directions}, url = {https://doi.org/10.1007/s11042-019-08373-8}, volume = 79, year = 2020 }