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UNIVERSITY OF ENGINEERING AND TECHNOLOGY 
VIETNAM NATIONAL UNIVERSITY, HANOI

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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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III

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 1. Introduction ... 1

1.1. Motivation ... 1

1.2. Sentiment Analysis Problems ... 2

1.2.1. Problem Description ... 2

1.2.2. Different Levels of Analysis ... 3

1.2.3. Natural Language Processing Issues ... 4

1.3. About This Thesis ... 4

1.3.1. Thesis Aims ... 4

1.3.2. Thesis structure ... 4

Chapter 2. Sentiment Analysis and Methods ... 6

2.1. Opinion Definition ... 6

2.2. Sentiment Analysis Tasks... 7

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 3. Subjective Document Detection ... 18

3.1. Subjectivity Classification problem ... 18

3.2. General Framework ... 18

3.3. Building the Classifier ... 20

Chapter 4. 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 5. 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

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[10]B. Liu. Sentiment analysis and subjectivity, available from />

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