Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
OpinionMiner: a novel machine learning system for web opinion mining and extraction
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Twitter power: Tweets as electronic word of mouth
Journal of the American Society for Information Science and Technology
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Proximity-based opinion retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Semi-Supervised Learning
IEEE Transactions on Knowledge and Data Engineering
Classifying sentiment in microblogs: is brevity an advantage?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Semi-Supervised Classification of Network Data Using Very Few Labels
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Sentiment analysis on twitter data for portuguese language
PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
Mining topic-level opinion influence in microblog
Proceedings of the 21st ACM international conference on Information and knowledge management
Semantic sentiment analysis of twitter
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Towards social imagematics: sentiment analysis in social multimedia
Proceedings of the Thirteenth International Workshop on Multimedia Data Mining
Polarity analysis of micro reviews in foursquare
Proceedings of the 19th Brazilian symposium on Multimedia and the web
Sentiment analysis on evolving social streams: how self-report imbalances can help
Proceedings of the 7th ACM international conference on Web search and data mining
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Real-time interaction, which enables live discussions, has become a key feature of most Web applications. In such an environment, the ability to automatically analyze user opinions and sentiments as discussions develop is a powerful resource known as real time sentiment analysis. However, this task comes with several challenges, including the need to deal with highly dynamic textual content that is characterized by changes in vocabulary and its subjective meaning and the lack of labeled data needed to support supervised classifiers. In this paper, we propose a transfer learning strategy to perform real time sentiment analysis. We identify a task - opinion holder bias prediction - which is strongly related to the sentiment analysis task; however, in constrast to sentiment analysis, it builds accurate models since the underlying relational data follows a stationary distribution. Instead of learning textual models to predict content polarity (i.e., the traditional sentiment analysis approach), we first measure the bias of social media users toward a topic, by solving a relational learning task over a network of users connected by endorsements (e.g., retweets in Twitter). We then analyze sentiments by transferring user biases to textual features. This approach works because while new terms may arise and old terms may change their meaning, user bias tends to be more consistent over time as a basic property of human behavior. Thus, we adopted user bias as the basis for building accurate classification models. We applied our model to posts collected from Twitter on two topics: the 2010 Brazilian Presidential Elections and the 2010 season of Brazilian Soccer League. Our results show that knowing the bias of only 10% of users generates an F1 accuracy level ranging from 80% to 90% in predicting user sentiment in tweets.