Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feat...Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.展开更多
Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusi...Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusion of EEG classifiers to improve the motor imagery EEG classification performance. Two feature extraction methods are employed to extract the feature from three different areas of EEG. One is power spectral density（PSD）, and the other is common spatial patterns（CSP）. Classifiers are designed based on the well-known linear discrimination analysis（LDA）. The fusion of the individual classifiers is realized by means of the Choquet fuzzy integral. It is demonstrated that the proposed method comes with better performance compared with the individual classifier.展开更多
基金National Natural Science Foundation of China(No.71331008)
文摘Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.
文摘Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusion of EEG classifiers to improve the motor imagery EEG classification performance. Two feature extraction methods are employed to extract the feature from three different areas of EEG. One is power spectral density（PSD）, and the other is common spatial patterns（CSP）. Classifiers are designed based on the well-known linear discrimination analysis（LDA）. The fusion of the individual classifiers is realized by means of the Choquet fuzzy integral. It is demonstrated that the proposed method comes with better performance compared with the individual classifier.
