Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
How to Build a Digital Library
How to Build a Digital Library
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Evolving GATE to meet new challenges in language engineering
Natural Language Engineering
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
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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This study develops an automatic method for in-depth sentiment analysis of movie review documents using information extraction techniques and a machine learning approach. The analysis results provide sentiment orientations in multiple perspectives, each focusing on a specific aspect of the reviewed entity. Sentiment classification in multiple perspectives can provide more comprehensive sentiment analysis for applications like sentiment ranking and rating. By utilizing information extraction techniques such as entity extraction, co-referencing and pronoun resolution, the review texts are segmented into sections where each section discusses particular aspect of the reviewed entity. For each section of sentences, Support Vector Machine (SVM) using vectors of terms is applied to determine sentiment orientation toward the target aspect. In our exploratory study, we focus on the sentiment orientations toward overall movie, movie directors and casts in the movie. The experimental results prove the effectiveness of the proposed approach for sentiment classification of movie reviews.