WordNet: a lexical database for English
Communications of the ACM
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European 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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
Modern Information Retrieval
Classifying sentiment in microblogs: is brevity an advantage?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
From bias to opinion: a transfer-learning approach to real-time sentiment analysis
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Content vs. context for sentiment analysis: a comparative analysis over microblogs
Proceedings of the 23rd ACM conference on Hypertext and social media
Twitter, MySpace, Digg: Unsupervised Sentiment Analysis in Social Media
ACM Transactions on Intelligent Systems and Technology (TIST)
Unsupervised sentiment analysis with emotional signals
Proceedings of the 22nd international conference on World Wide Web
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On Foursquare, one of the currently most popular location-based social networks, users can not only share which places (venues) they visit but also leave short comments (tips) about their previous experiences at specific venues. Tips may provide a valuable feedback for business owners as well as for potential new customers. Sentiment or polarity classification provides useful tools for opinion summarization, which can help both parties to quickly obtain a predominant view of the opinions posted by users at a specific venue. We here present what, to our knowledge, is the first study of polarity of Foursquare tips. We start by characterizing two datasets of collected tips with respect to their textual content. Some inherent characteristics of tips, such as short sizes as well as informal and often noisy content, pose great challenges to polarity detection. We then investigate the effectiveness of four alternative polarity classification strategies on subsets of our dataset. Three of the considered strategies are based on supervised machine learning techniques and the fourth one is an unsupervised lexicon-based approach. Our evaluation indicates that effective polarity classification can be achieved even if the simpler lexicon-based approach, which does not require costly manual tip labeling, is adopted.