Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Convex Optimization
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
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
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Tweet the debates: understanding community annotation of uncollected sources
WSM '09 Proceedings of the first SIGMM workshop on Social media
Exploiting internal and external semantics for the clustering of short texts using world knowledge
Proceedings of the 18th ACM conference on Information and knowledge management
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Latent aspect rating analysis on review text data: a rating regression approach
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
Lexicon-based methods for sentiment analysis
Computational Linguistics
Identifying noun product features that imply opinions
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Twitter polarity classification with label propagation over lexical links and the follower graph
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
MoodLens: an emoticon-based sentiment analysis system for chinese tweets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting homophily effect for trust prediction
Proceedings of the sixth ACM international conference on Web search and data mining
Exploiting social relations for sentiment analysis in microblogging
Proceedings of the sixth ACM international conference on Web search and data mining
Whoo.ly: facilitating information seeking for hyperlocal communities using social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Comparing and combining sentiment analysis methods
Proceedings of the first ACM conference on Online social networks
Polarity analysis of micro reviews in foursquare
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications. It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving. Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words. Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals. In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals.