Data Set Used
In this paper, we address a relatively new and interesting text categorization problem: classify a political blog as either <i>liberal</i> or <i>conservative</i>, based on its political leaning. Our subjectivity analysis based method is twofold: 1) we identify subjective sentences that contain at least two strong subjective clues based on the General… (More)
— This paper addresses a relatively new text categorization problem: classifying a political blog as either 'liberal' or 'conservative', based on its political leaning. Instead of simply using " Bag of Words " features (BoW) as in previous work, we have explored subjectivity manifested in blogs and used subjectivity information thus found to help build… (More)
In this paper, we present a series of semantic analyses of words in political blogs in the setting of categorization of two opposite political orientations: liberal vs. conservative. We classify nouns, verbs, adjectives and adverbs into semantic categories by using the General Inquirer dictionary. Then distributions of these categories and correlations… (More)
This workshop seeks to bring together researchers in both computer science and social sciences who are interested in developing and using topic-sentiment analysis methods to measure mass opinion, and to foster communications between the research community and industry practitioners as well.
The TREC Legal Track 2009 features an Interactive Task that is designed to replicate real-world challenges in producing a collection of responsive documents from a large collection of documents. The task required us to produce responsive documents from any of the seven topics, which are production requests. Clearwell Systems incorporated novel methods for… (More)
We hypothesize that the variance in volume of high-velocity queries over time can be explained by observing that these queries are formulated in response to events in the world that users are interested in. Based on it, this paper describes a system, ZED, which automatically finds explanations for high velocity queries, by extracting descriptions of… (More)