A maximum entropy approach to natural language processing
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
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
Automatic mood detection and tracking of music audio signals
IEEE Transactions on Audio, Speech, and Language Processing
Toward intelligent music information retrieval
IEEE Transactions on Multimedia
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In recent years, efficient and intelligent music information retrieval became very important. One essential aspect of this field is music emotion classification by earning from lyrics. This problem is different from traditional text categorization in that more linguistic or semantic information is required for better emotion analysis. Therefore, we focus on how to extract useful and meaningful language features in this paper. We investigate three kinds of preprocessing methods and a series of language grams having different n-order under the well-known n-gram language model framework to extract more semantic features. Then, we employ three supervised learning methods (Naïve Bayes, maximum entropy classification, and support vector machines) to examine the classification performance. Experimental results show that feature extraction methods improve music emotion classification accuracies. Maximum entropy classification with unigram+bigram+trigram gets best accuracy and it is suitable for music emotion classification.