WordNet: a lexical database for English
Communications of the ACM
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
IEEE Intelligent Systems
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
Semantic oriented clustering of documents
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Sentic Computing: Techniques, Tools, and Applications
Sentic Computing: Techniques, Tools, and Applications
Cross-Domain Sentiment Classification Using a Sentiment Sensitive Thesaurus
IEEE Transactions on Knowledge and Data Engineering
New Avenues in Opinion Mining and Sentiment Analysis
IEEE Intelligent Systems
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The automatic detection of orientation and emotions in texts is becoming increasingly important in the Web 2.0 scenario. There is a considerable need for innovative techniques and tools capable of identifying and detecting the attitude of unstructured text. The paper tackles two crucial aspects of the sentiment classification problem: first, the computational complexity of the deployed framework; second, the ability of the framework itself to operate effectively in heterogeneous commercial domains. The proposed approach adopts empirical learning to implement the sentiment-classification technology, and uses a distance-based predictive model to combine computational efficiency and modularity. A suitably designed semantic-based metric is the cognitive core that measures the distance between two user reviews, according to the sentiment they communicate. The framework ultimately nullifies the training process; at the same time, it takes advantage of a classification procedure whose computational cost increases linearly when the training corpus increases. To attain an objective measurement of the actual accuracy of the sentiment classification method, a campaign of tests involved a pair of complex, real-world scoring domains; the goal was to compare the predicted sentiment scores with actual scores provided by human assessors. Experimental results confirmed that the overall approach attained satisfactory performances in terms of both cross-domain classification accuracy and computational efficiency.