DSpace at VNU: Transductive support vector machines for cross-lingual sentiment classification

Transductive support vector machines for cross-lingual sentiment classification Nguyễn Thị Thùy Linh Trường Đại học Công nghệ Luận văn Thạc sĩ ngành: Khoa học máy tính; Mã số: 60 48 01

Transductive support vector machines for cross-lingual sentiment classification

Nguyễn Thị Thùy Linh

Trường Đại học Công nghệ Luận văn Thạc sĩ ngành: Khoa học máy tính; Mã số: 60 48 01

Người hướng dẫn: PGS.TS Hà Quang Thụy

Năm bảo vệ: 2009

Abstract 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

Keywords Khoa học máy tính; Công nghệ thông tin; Dữ liệu; Ngôn ngữ

Content

Table of Contents

1.1 Introduction 1

1.2 What might be involved? 3

1.3 Our approach 3

1.4 Related works 4

1.4.1 Sentiment classification 4

1.4.1.1 Sentimentclassification tasks 4

1.4.1.2 Sentimentclassification features 4

1.4.1.3 Sentimentclassification techniques 4

B 32

1.4.1.4 Sentimentclassificationdomains 5

1.4.2 Cross-domain text classification 5

2 Background 6 2.1 Sentiment Analysis 6

2.1.1 Applications 7

2.2 Support Vector Machines 7

2.3 Semi-supervised techniques 10

2.3.1 Generate maximum-likelihood models 10

2.3.2 Co-training and bootstrapping 11

2.3.3 Transductive SVM 11

3 The semi-supervised modelfor cross-lingual approach 13 3.1 The semi-supervised model 13

3.2 Review Translation 16

3.3 Features 16

3.3.1 Words Segmentation 16

3.3.2 Part of Speech Tagging 18

3.3.3 N-gram model 18

4 Experiments 20

4.1 Experimental set up 20

4.2 Data sets 20

4.3 Evaluation metric 22

4.4 Features 22

4.5 Results 23

4.5.1 Effect of cross-lingual corpus 23

4.5.2 Effect of extraction features 24

4.5.2.1 Using stopword list 24

4.5.2.2 Segmentation and Part of speech tagging 24

4.5.2.3 Bigram 25

4.5.3 Effect of features size 25

5 Conclusion andFuture Works 28 A 30

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