The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
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
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
What emotions do news articles trigger in their readers?
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Tapping on the potential of q&a community by recommending answer providers
Proceedings of the 17th ACM conference on Information and knowledge management
Emotion Classification of Online News Articles from the Reader's Perspective
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Ranking reader emotions using pairwise loss minimization and emotional distribution regression
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
SemEval-2007 task 14: affective text
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
SWAT-MP: the SemEval-2007 systems for task 5 and task 14
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Joint sentiment/topic model for sentiment analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Joint Emotion-Topic Modeling for Social Affective Text Mining
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
An exploration of features for recognizing word emotion
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
Automatically extracting polarity-bearing topics for cross-domain sentiment classification
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Mining Social Emotions from Affective Text
IEEE Transactions on Knowledge and Data Engineering
Towards jointly extracting aspects and aspect-specific sentiment knowledge
Proceedings of the 21st ACM international conference on Information and knowledge management
Multi-label ensemble based on variable pairwise constraint projection
Information Sciences: an International Journal
Techniques and applications for sentiment analysis
Communications of the ACM
Probabilistic generative ranking method based on multi-support vector domain description
Information Sciences: an International Journal
New Avenues in Opinion Mining and Sentiment Analysis
IEEE Intelligent Systems
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The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers' emotions evoked by social media materials. Compared to the classical sentiment analysis from writers' perspective, sentiment analysis of readers is sometimes more meaningful in social media. We propose two sentiment topic models to associate latent topics with evoked emotions of readers. The first model which is an extension of the existing Supervised Topic Model, generates a set of topics from words firstly, followed by sampling emotions from each topic. The second model generates topics from social emotions directly. Both models can be applied to social emotion classification and generate social emotion lexicons. Evaluation on social emotion classification verifies the effectiveness of the proposed models. The generated social emotion lexicon samples further show that our models can discover meaningful latent topics exhibiting emotion focus.