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In this research field, there are some essential problems including: subjectivity classification, polarity classification, aspect based sentiment analysis, sentiment rating.. Th[r]
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UNIVERSITY OF ENGINEERING AND TECHNOLOGY VIETNAM NATIONAL UNIVERSITY, HANOI
PHAM DINH TAI
SENTIMENT ANALYSIS USING NEURAL NETWORK
MASTER OF COMPUTER SCIENCE
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UNIVERSITY OF ENGINEERING AND TECHNOLOGY VIETNAM NATIONAL UNIVERSITY, HANOI
PHAM DINH TAI
SENTIMENT ANALYSIS USING NEURAL NETWORK
Major: Computer Science Code : 60.48.01.01
MASTER OF COMPUTER SCIENCE
Supervisor: Assoc Prof Dr Le Anh Cuong
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ORIGINALITY STATEMENT
I hereby declare that this submission is my own work and to the best of my knowledge, it contains no materials previously published or written by another person, or substantial proportions of material which has been accepted for the award of any other degree or diploma at University of Engineering and Technology (UET), or any other educational institution, except where due acknowledgement is made in the thesis Any contribution made to the research by others, with whom I have studied at UET or elsewhere, is explicitly acknowledged in the thesis I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged
Signature
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Abstract
Sentiment analysis and opinion mining is an important task in natural language processing and data mining Opinions of users’ comments from social network, forum, blog, … are very useful for new user when they are looking for a good service or good product It is also useful for service providers or companies for improving their products based on comments from customers
Therefore, recently there have been raising a large number of studies focusing on the problem of opinion mining and sentiment analysis In this research field, there are some essential problems including: subjectivity classification, polarity classification, aspect based sentiment analysis, sentiment rating
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Acknowledgements
First and foremost I would like to offer my sincerest gratitude to my supervisor, Assoc.Prof.Dr Le Anh Cuong who always supported me throughout my research with patience He always appears when I need help, and responds to queries so helpfully and promptly I attribute the level of my Master’s degree to him encouragement and effort Without him, this thesis would not have come into being I could never wish for better or kinder supervisors
I would like to give my honest appreciation to my group friends: Le Ngoc Anh, Nguyen Ngoc Truong, Dao Bao Linh who study in my school for what so ever they did for me
I am very grateful to Mrs.Nguyen Thi Xuan Huong and Mr.Pham Duc Hong, graduate students at University of Engineering and Technology(UET), and for providing me the methods and data required for sentiment analysis
Special thanks to Trinh Quyet Thang student at University of Engineering and Technology (UET) for providing me the forum data and help me source code required for sentiment analysis
Last but not least, I am very grateful to my family who love them the most in this world People I cannot imagine living my life without them
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Contents
Acknowledgements III Contents IV List of Tables VI List of Figures VII List of Abbreviations VIII
Chapter Introduction
1.1 Motivation
1.2 Sentiment Analysis Problems
1.2.1 Problem Description
1.2.2 Different Levels of Analysis
1.2.3 Natural Language Processing Issues
1.3 About This Thesis
1.3.1 Thesis Aims
1.3.2 Thesis structure
Chapter Sentiment Analysis and Methods
2.1 Opinion Definition
2.2 Sentiment Analysis Tasks
2.3 Subjectivity and Emotion 10
2.4 Document Sentiment Classification 13
2.4.1 Sentiment Classification Using Supervised Learning 13
2.4.2 Sentiment Rating Prediction 15
2.5 Dictionary based Approach & Corpus Approach 16
Chapter Subjective Document Detection 18
3.1 Subjectivity Classification problem 18
3.2 General Framework 18
3.3 Building the Classifier 20
Chapter Sentiment Analysis with Neural Networks 23
4.1 Neural Network 23
4.2 Problem of Sentiment Rating 26
4.2.1 Formulating the Problem 27
Chapter Experiments 29
5.1 Data set 29
5.2 Sentiment Analysis with Subjectivity 29
5.2.1 Data presentation 29
5.2.2 Feature extraction: 31
5.2.3 Experimental Results 31
5.3 Sentiment analysis with ratings 32
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List of Tables
Table 5.1 Data set 30
Table 5.2 Result machine learning 31
Table 5.3 Result using perceptron with 200 loops 32
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REFERENCES
[1] Hu, Minqing and Bing Liu Mining and summarizing customer reviews in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004) 2004
[2] Riloff, Ellen, Siddharth Patwardhan, and Janyce Wiebe Feature subsumption for opinion analysis in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2006) 2006
[3] Wiebe, Janyce and Ellen Riloff Creating subjective and objective sentence classifiers from unannotated texts.Computational Linguistics and Intelligent Text Processing, p 486-497 2005
[4] Zhang, Lei and Bing Liu Identifying noun product features that imply opinions in Proceedings of the Annual Meeting of the Association for Computational Linguistics (short paper) (ACL-2011) 2011b
[5] Parrott, W Gerrod Emotions in social psychology: Essential readings: Psychology Pr 2001
[6] Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan Thumbs up? sentiment classification using machine learning techniques in Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2002) 2002
[7] Pang, Bo and Lillian Lee Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales in Proceedings of Meeting of the Association for Computational Linguistics (ACL-2005) 2005
[8] Goldberg, Andrew B and Xiaojin Zhu Seeing stars when there aren't many stars: graph-based semi-supervised learning for sentiment categorization in Proceedings of HLT-NAACL 2006 Workshop on Textgraphs: Graph-based Algorithms for Natural Language Processing 2006
[9] Wan, Xiaojun Co-training for cross-lingual sentiment classification in Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP (ACL-IJCNLP-2009) 2009
[10]B Liu Sentiment analysis and subjectivity, available from http://www.cs.uic.edu/
liub/FBS/NLP-handbook-sentiment-analysis.pdf, viewed on 30/08/2011
[11] M.H Hassoun Fundamentals of artificial neural networks the MIT Press, 1995 [12]Onix text retrieval toolkit stopword list
http://www.lextek.com/manuals/onix/stopwords1.html