Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Ontology-focused crawling of Web documents
Proceedings of the 2003 ACM symposium on Applied computing
Probabilistic models for focused web crawling
Proceedings of the 6th annual ACM international workshop on Web information and data management
Exploiting Interclass Rules for Focused Crawling
IEEE Intelligent Systems
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
ACM Transactions on Information Systems (TOIS)
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Adaptive geospatially focused crawling
Proceedings of the 18th ACM conference on Information and knowledge management
Domain-specific sentiment analysis using contextual feature generation
Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
Proceedings of the 20th ACM international conference on Information and knowledge management
A large-scale sentiment analysis for Yahoo! answers
Proceedings of the fifth ACM international conference on Web search and data mining
Analyzing, Detecting, and Exploiting Sentiment in Web Queries
ACM Transactions on the Web (TWEB)
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The sentiments and opinions that are expressed in web pages towards objects, entities, and products constitute an important portion of the textual content available in the Web. Despite the vast interest in sentiment analysis and opinion mining, somewhat surprisingly, the discovery of the sentimental or opinionated web content is mostly ignored. This work aims to fill this gap and address the problem of quickly discovering and fetching the sentimental content present in the Web. To this end, we design a sentiment-focused web crawling framework for faster discovery and retrieval of such content. In particular, we propose different sentiment-focused web crawling strategies that prioritize discovered URLs based on their predicted sentiment scores. Through simulations, these strategies are shown to achieve considerable performance improvement over general-purpose web crawling strategies in discovering sentimental content.