Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4 - Volume 04
A novel refinement approach for text categorization
Proceedings of the 14th ACM international conference on Information and knowledge management
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
A novel scheme for domain-transfer problem in the context of sentiment analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
An empirical study of sentiment analysis for chinese documents
Expert Systems with Applications: An International Journal
Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
A survey on sentiment detection of reviews
Expert Systems with Applications: An International Journal
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Proceedings of the third ACM international conference on Web search and data mining
Lexicon-based Comments-oriented News Sentiment Analyzer system
Expert Systems with Applications: An International Journal
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In recent years, Structural Correspondence Learning (SCL) is regarded as one of the most promising techniques for transfer learning. The main idea behind SCL model is to identify correspondences among features from different domains by modeling their correlations with pivot features. However, SCL model treats each feature as well as each instance by an equivalent-weight strategy. From the perspective of feature, this strategy fails to overcome the adverse influence of high-frequency domain-specific (HFDS) features: they occupy a relative large portion of weight in classification model, while hardly carry corresponding sentiment information. From the other perspective, the equivalent-weight strategy of SCL model does not take into account the labels (''positive'' or ''negative'') of labeled instance and the labels of pivot features: positive pivot features tend to occur more frequently in positive instances and vice versa. To address the two issues effectively, we proposed a weighted SCL model (W-SCL), which weights the features as well as the instances. More specifically, W-SCL assigns a smaller weight to HFDS features and assigns a larger weight to instances with the same label as the involved pivot feature. The experimental results indicate that proposed W-SCL model could overcome the adverse influence of HFDS features, and leverage knowledge from labels of instances and pivot features.