Evaluating the Usefulness of Sentiment Information for Focused Crawlers
This work addresses the need for improved business and marketing intelligence gathering in the Web 2.0 era by enhancing focused crawling for opinion-rich content, though it appears incremental as it builds on existing focused crawling methods.
The study tackled the problem of effectively collecting sentiment-related web content by evaluating focused crawlers that use sentiment information and sentiment-labeled web graphs, resulting in more holistic collections of opinion-related content on a large test bed of over 500,000 web pages.
Despite the prevalence of sentiment-related content on the Web, there has been limited work on focused crawlers capable of effectively collecting such content. In this study, we evaluated the efficacy of using sentiment-related information for enhanced focused crawling of opinion-rich web content regarding a particular topic. We also assessed the impact of using sentiment-labeled web graphs to further improve collection accuracy. Experimental results on a large test bed encompassing over half a million web pages revealed that focused crawlers utilizing sentiment information as well as sentiment-labeled web graphs are capable of gathering more holistic collections of opinion-related content regarding a particular topic. The results have important implications for business and marketing intelligence gathering efforts in the Web 2.0 era.