Novel Web Metrics Based On Sentiment Analysis

Abstract

In recent years, the Internet has been experiencing a huge boom in social networking, blogging and discussing on online forums. With the growing popularity of these communication channels, there have been arising a large number of comments on various topics from many different types of users. Such information source is not only useful for academic researchers, but also for commercial companies that would like to gain a direct user feedback on price, quality, and other factors of their products. However, obtaining comprehensive information from such a source is a challenging task nowadays. Several models have been proposed for the social media analysis on the Web. However, many of these solutions are usually tailored to a specific purpose or data type, and there is still lack of generality and unclear approach to handling the data. Moreover, a web content diversity, a variety of technologies along with the website structure differences, all of these make the Web a network of heterogeneous data, where things are difficult to find. It is, therefore, necessary to design a suitable metric that would reflect a semantic content of single pages in a better way. In this thesis, the main emphasis has been placed on the evaluation of the Internet trends, where the trend may be defined as anything from an event, product name, name of a person or any expression, which is mentioned online. A general model has been proposed to collect and analyse data from the Web. The analysis part of the model is based on webometric principles that are enhanced by the methods of sentiment and social network analysis. The extension of webometrics by the combination of these methods leads up to gaining insights into the public opinion with respect to some topic, and to a better machine understanding of a text. iii In particular, the main contributions of the dissertation thesis are as follows: 1. Proposal of the new theoretical model for gathering and processing data from Web 2.0. 2. Definition of the methodology for the evaluation of Internet trends. 3. Adaptation of the newly designed methodology for the evaluation in social network sphere. 4. Proposal of the new sentiment sense disambiguation methods to improve sentiment classification for multiple-topic related words. 5. Architecture design of the new framework that provides an end-to-end approach to the analysis of selected Internet trends.