SENTIMENT ANALYSIS OF ENGLISH TWEETS USING DATA MINING

Data Mining, Sentiment Analysis

Table of Contents

Abstract

Table of Contents

List of Figures

List of Tables

Chapter 1 INTRODUCTION

1.1 Introduction to Data Mining

1.1.1 Process of Data Mining

1.1.2 Applications of Data Mining

1.1.3 Data Mining Hierarchical Model

1.2 Introduction to Sentiment Analysis

1.2.1 Components of Sentiment Analysis

1.2.2 Level of Sentiment Analysis       

1.2.3 Classification of Sentiment Analysis

1.2.4 Techniques for sentiment Classification

1.2.5 Application Areas

Chapter 2 SURVEY OF LITERATURE

2.1 Introduction

2.2 Related work

2.3 Summary

Chapter 3 METHODOLOGY

3.1 Methodology

3.1.1 Create Dictionary

3.1.2 Tweets Collection

3.1.3 Data Pre-processing

3.1.3.1 Filtering

3.1.3.2 Twitter slang removal

3.1.3.3 Stop words removal

3.1.3.4 Negation Handling

3.1.3.5 Stemming

3.1.3.6 Example for tweets pre-processing

3.1.3.7 Calculating sentiment score

3.2 Algorithm for sentiment Analysis

Chapter 4 IMPLEMENTATION

4.1 Netbeans IDE Interface             

4.2 Main window                            

4.3 Dictionary Creation                    

4.3.1 Positive words dictionary         

4.3.2 Negative words dictionary       

4.4 Slang words table                     

4.5 Stop words table                       

4.6 Tweets dataset                         

4.6.1 IPhone tweets table                

4.6.2 Cricket tweets table                

4.6.3 Badminton tweets table          

4.6.4 Bahuballi2 tweets table           

4.6.5Qismat Punjabi song tweets table

4.6.6Ishqbaaz Hindi serial tweets        

Chapter 5 RESULTS & DISCUSSIONS

5.1 Results for IPhone dataset               

5.2 Results for Bahuballi2 movie dataset 

5.3 Results for Cricket dataset               

5.4 Results for Badminton dataset          

5.5 Results for Ishqbaaz dataset            

5.6 Results for Qismat song dataset       

5.7 Accuracy comparison of different datasets

5.8 Detail of 6 datasets                                

Chapter 6 CONCLUSION & FUTURE SCOPE

6.1 Conclusion     

6.2 Challenges     

6.3 Future Scope  

List of Figures

Figure 1.1:     Process of Data Mining

Figure 1.2:     Data mining hierarchical model

Figure 1.3:     Components of sentiment analysis

Figure 1.4:     Positive, Neutral & Negative sentiment

Figure 3.1:     Architecture of proposed system

Figure 3.2:     Flow chart of the system

Figure 4.1:     Netbeans IDE Interface

Figure 4.2:     Main executable window

Figure 4.3:     Positive words table

Figure 4.4:     Negative words table

Figure 4.5:     Slang words table

Figure 4.6:     Stop words table

Figure 4.7:     Sentiment140 tool

Figure 4.8:     Sentiment140 tool after login to twitter

Figure 4.9:     IPhone tweets table

Figure 4.10:   Cricket tweets table

Figure 4.11:   Badminton tweets table

Figure 4.12:   Bahuballi2 movie tweets

Figure 4.13:   Qismat song tweets

Figure 4.14:   Ishqbaaz serial tweets

Figure 5.1:     Result of IPhone tweets

Figure 5.2:     Pie chart for IPhone tweets

Figure 5.3:     Results of Bahuballi2 movie tweets

Figure 5.4:     Pie chart for Bahuballi2 movie tweets

Figure 5.5:     Result of Cricket tweets

Figure 5.6:     Pie chart for cricket tweets

Figure 5.7:     Result of Badminton tweets

Figure 5.8:     Pie chart for Badminton tweets

Figure 5.9:     Results for Ishqbaaz serial

Figure 5.10:   Pie chart of Ishqbaaz serial tweets

Figure 5.11:   Results of Qismat song tweets

Figure 5.12:   Pie chart for Qismat song tweets

Figure 5.13:   Bar chart showing accuracy of different datasets

Figure 5.14:   Graphical representation of results

List of Tables

Table 2.1:        Summary of Literature Review

Table 3.1:        Database table

Table 3.2:        Positive words table

Table 3.3:        Negative words table

Table 3.4:        IPhone sentiment score database table

Table 3.5:        Data Filtering

Table 3.6:        Slang removal

Table 3.7:        Stemming

Table 3.8:        Example for tweets pre-processing

Table 5.1:        Result table