Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Analyzing the effectiveness and applicability of co-training
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Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
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
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Co-training for cross-lingual sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Characterizing debate performance via aggregated twitter sentiment
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cross-domain sentiment classification via spectral feature alignment
Proceedings of the 19th international conference on World wide web
Employing personal/impersonal views in supervised and semi-supervised sentiment classification
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Structure-aware review mining and summarization
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Journal of the American Society for Information Science and Technology
Filling the gap: semi-supervised learning for opinion detection across domains
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
A cross-domain adaptation method for sentiment classification using probabilistic latent analysis
Proceedings of the 20th ACM international conference on Information and knowledge management
Email classification with co-training
Proceedings of the 2011 Conference of the Center for Advanced Studies on Collaborative Research
A large-scale sentiment analysis for Yahoo! answers
Proceedings of the fifth ACM international conference on Web search and data mining
Incorporating reviewer and product information for review rating prediction
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Semi-supervised learning for imbalanced sentiment classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Predicting collective sentiment dynamics from time-series social media
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Aspect extraction through semi-supervised modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Cross-domain co-extraction of sentiment and topic lexicons
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Sentiment classification is an important problem in tweets mining. There lack labeled data and rating mechanism for generating them in Twitter service. And topics in Twitter are more diverse while sentiment classifiers always dedicate themselves to a specific domain or topic. Thus it is a challenge to make sentiment classification adaptive to diverse topics without sufficient labeled data. Therefore we formally propose an adaptive multiclass SVM model which transfers an initial common sentiment classifier to a topic-adaptive one. To tackle the tweet sparsity, non-text features are explored besides the conventional text features, which are intuitively split into two views. An iterative algorithm is proposed for solving this model by alternating among three steps: optimization, unlabeled data selection and adaptive feature expansion steps. The algorithm alternatively minimizes the margins of two independent objectives on different views to learn coefficient matrices, which are collaboratively used for unlabeled tweets selection from the topic that the algorithm is adapting to. And then topic-adaptive sentiment words are expended based on the above selection, in turn to help the first two steps find more confident and unlabeled tweets and boost the final performance. Comparing with the well-known supervised sentiment classifiers and semi-supervised approaches, our algorithm achieves promising increases in accuracy averagely on the 6 topics from public tweet corpus.