Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Transforming the social networking experience with sensing presence from mobile phones
Proceedings of the 6th ACM conference on Embedded network sensor systems
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Timelines as summaries of popular scheduled events
Proceedings of the 22nd international conference on World Wide Web companion
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This paper proposes realtime estimation of support rate based on social sensors. Nowadays, micro blogs like Twitter have gained wide popularity, especially among the youth for its capability of updating personal opinions in a realtime manner. Academically, they have received tremendous attention as well. We argue that realtime events that have great influence on the attitudes of Twitter users can be detected by strategically monitoring tweets on certain topics. Building on the collected data, sentiment analysis enables us to calculate percentage of positive tweets, namely, support rate. In particular, given Twitter's realtime nature, the support rate calculation shall also be done in realtime. Drawing on World Cup 2010, we collect a large amount of tweets and carry out analysis so as to extract sentiment information of the audience and go further to show the realtime support rate of the participators.