Demonstration of hierarchical document clustering of digital library retrieval results
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Learning to cluster web search results
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Filtering product reviews from web search results
Proceedings of the 2007 ACM symposium on Document engineering
Effectiveness of web search results for genre and sentiment classification
Journal of Information Science
Automatic classification of web search results: product review vs. non-review documents
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
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Several researchers have developed tools for classifying/ clustering Web search results into different topic areas (such as sports, movies, travel, etc.), and to help users identify relevant results quickly in the area of interest. This study follows a similar approach, but is in the area of sentiment classification -- automatically classifying on-line review documents according to the overall sentiment expressed in them. This paper presents a prototype system that has been developed to perform sentiment categorization of Web search results. It assists users to quickly focus on recommended (or non-recommended) information by classifying Web search results into four categories: positive, negative, neutral, and non-review documents, by using an automatic classifier based on a supervised machine learning algorithm, Support Vector Machine (SVM).