Web mining: Pattern discovery on the world wide web

The uncountable size of the data in the World Wide Web (WWW) nowadays makes it the largest cloud database that ever existed on Earth. The problem with data is that it is not a structured database, which makes it meaningless. To make the data usable, web mining methods are created. Web mining is the application of data mining techniques to discover patterns from the World Wide Web (WWW). Web mining is a powerful research area to gather and examine content from web pages or web services. It has methods for information retrieval from web pages and analyses the structure of gathered documents. Moreover, web mining gathers data related to the structure of a website and its users using the web-server logs and session logs.However, although reaching data from the WWW is possible with web mining techniques, the reached data might not be sensible or meaningful without machine learning techniques. To make the data sensitive and meaningful, there exist a lot of methods depending on one?s aims. Classification, which can classify web data according to its content, is one of most popular data mining methods in machine learning.This thesis proposes the hybrid combination of web mining techniques and machine learning techniques. The developed approach can gather Turkish text data from various web pages and web services and serve it in a structured data format. The study in this thesis basically covers web content mining, web structure mining for gathering data and analyzing the structure of web pages and services. It also uses various internal and external web services for language detection, Turkish spell-checking, Turkish `Part of Speech Tagging? (pos-tagging) and stemming operations. Moreover, the study uses two machine learning techniques, which are Naïve Bayes and `Support Vector Machines? with weighting method of TF-IDF (Term Frequency ? Inverse Document Frequency)?, to sentimentally classify the data gathered from web pages.In this work, firstly, how and where the data is gathered is given. Secondly, the operations over the text data are explained in detail. Then, finally, sentimental classification with accuracy values over the gathered data with multiple perspectives is given.Keywords: Web Mining, Web Content Mining, Web Structure Mining, Feedback Mining, Sentimental Classification