Luận văn:Transductive support vector machines for cross-lingual sentiment classification : Luận văn ThS. Công nghệ thông tin: 60 48 01
Nhà xuất bản:ĐHCN
Ngày:2009
Chủ đề:Khoa học máy tính
Công nghệ thông tin
Dữ liệu
Ngôn ngữ
Miêu tả:36 p. + CD-ROM
Luận văn ThS. Khoa học máy tính -- Trường Đại học Công nghệ. Đại học Quốc gia Hà Nội, 2009
Sentiment classification has been much attention and has many useful applications on business and intelligence. This thesis investigates sentiment classification problem employing machine learning technique. Since the limit of Vietnamese sentiment corpus, while there are many available English sentiment corpus on the Web. We combine English corpora as training data and a number of unlabeled Vietnamese data in semi-supervised model. Machine learning eliminates the language gap between the training set and test set in our model. Moreover, we also examine types of features to obtain the best performance. The results show that semi-supervised classifier are quite good in leveraging cross-lingual corpus to compare with the classifier without cross-lingual corpus. In term of features, we find that using only unigram model turning out the outperformace
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