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A DYNAMIC MODEL OF SOCIAL MEDIA MONITORING TOOLS WITH SENTIMENT ANALYSIS
posted on SEPTEMBER 13, 2021
ABSTRACT
The proposed system is a dynamic model of social media monitoring tool with sentiment analysis. Social media platforms contain a lot of data which might be considered ambiguous to businesses/organizations. Most organizations make use of social media platforms for advertisement as they reach a large crowd in a short period of time and are also more affordable compared to other options. Businesses find it difficult to have a clear vision/insight on how well their business has grown after each advertisement or product release. The aim of the project is to provide various relevant analysis including sentiment analysis on real-time data from social media platforms for specified keywords or social media accounts defined by registered users. Sentiment analysis is a process in which an algorithm is used to determine the emotion of texts i.e. positive, negative or neutral. Sentiment analysis helps businesses understand how their customers feel about their product. Businesses can also compare their products with their competitors to have a clearer insight on how their services are doing online compared to other businesses. This application was developed using the increment methodology and successfully produced the desirable results for the first iteration of the project. To accomplish this project Python language and PostgreSQL database are used. The end-result of the first iteration is focused on Twitter data. It performs all the requirements specified. At the end the application passed through different types of tests and the obtained accuracy was 98%. The application will continue with future enhancements and the other iterations.
TABLE OF CONTENTS
ABSTRACT vi
LIST OF TABLES IX
LIST OF FIGURES X
LIST OF ABBREVIATIONS XI
CHAPTER 1: INTRODUCTION 1
- OVERVIEW 1
- BACKGROUND AND MOTIVATION 1
- STATEMENT OF THE PROBLEM 2
- AIM AND OBJECTIVES 3
- SIGNIFICANCE OF THE PROJECT 4
- PROJECT RISKS ASSESSMENT 5
- SCOPE/PROJECT ORGANIZATION 7
CHAPTER 2: LITERATURE REVIEW 8
- INTRODUCTION 8
- HISTORICAL OVERVIEW 8
- RELATED WORK 9
- SUMMARY 13
CHAPTER 3: REQUIREMENTS ANALYSIS AND DESIGN 14
- OVERVIEW 14
- PROPOSED METHODOLOGY 14
- APPROACH TO CHOSEN METHODOLOGY/METHODS 19
- TOOLS AND TECHNIQUES 21
- ETHICAL CONSIDERATION 22
- REQUIREMENT ANALYSIS 23
- REQUIREMENTS SPECIFICATIONS 24
- Functional Requirement Specifications 24
- Non-Functional Requirement Specifications 26
- SYSTEM DESIGN 26
- Application Architecture 26
- Use Case 27
- Activity Diagrams 30
- Data Flow Diagram 31
- Entity-Relationship Diagram (ERD) 34
- User Interface Design 35
- Summary 39
CHAPTER 4: IMPLEMENTATION AND TESTING 40
| 4.1 OVERVIEW | 40 |
| 4.2 MAIN FEATURES | 40 |
| 4.3 IMPLEMENTATION PROBLEMS | 43 |
| 4.4 OVERCOMING IMPLEMENTATION PROBLEMS | 44 |
| 4.5 TESTING | 44CH |
- Tests Plans (for Unit Testing, Integration Testing, and System Testing) 45Test Suite (for Unit Testing, Integration Testing, and System Testing) 46Test Traceability Matrix (for Unit Testing, Integration Testing, and System Testing) 74Test Report Summary (for Unit Testing, Integration Testing, and System Testing) 76Error Reports and Corrections 76
- USE GUIDE 77
- SUMMARY 77
CHAPTER 5: DISCUSSION, CONCLUSION, AND RECOMMENDATIONS 78
- OVERVIEW 78
- OBJECTIVE ASSESSMENT 78
- LIMITATIONS AND CHALLENGES 78
- FUTURE ENHANCEMENTS 78
- RECOMMENDATIONS 79
- CONCLUSION 80
- SUMMARY 80
REFERENCES 81
APPENDICES 83
LIST OF TABLES
| TABLE 1 | RISK ASSESSMENT | 5 |
| TABLE 2 | SENTIMENT ANALYSIS TOOLS/TECHNIQUES | 12 |
| TABLE 3.1 | BRAINSTORMING REPORT | 20 |
| TABLE 3.2 | FUNCTIONAL REQUIREMENT SPECIFICATIONS | 23 |
| TABLE 3.3 | NON-FUNCTIONAL REQUIREMENT SPECIFICATIONS | 25 |
| TABLE 4.1 | TEST SUITE PERFORMED FOR REGISTER | 45 |
| TABLE 4.2 | TEST SUITE PERFORMED FOR LOGIN | 47 |
| TABLE 4.3 | TEST SUITE PERFORMED TO ADD KEYWORD TO USERS’ DASHBOARD | 48 |
| TABLE 4.4 | TEST SUITE PERFORMED TO ADD TWITTER ACCOUNT TO USERS’ DASHBOARD | 50 |
| TABLE 4.5 | TEST SUITE PERFORMED TO EXTRACT LATEST 100 TWEETS FROM TWITTER BASED ON KEYWORD | 52 |
| TABLE 4.6 | TEST SUITE PERFORMED TO DISPLAY DATA VISUALIZATION FOR NUMBER OF POSTS PER DAY | 54 |
| TABLE 4.7 | TEST SUITE PERFORMED TO PERFORM SENTIMENT ANALYSIS OF POST EXTRACTED | 56 |
| TABLE 4.8 | TEST SUITE PERFORMED TO CATEGORIZE TWEETS INTO TYPE OF DEVICE USED | 57 |
| TABLE 4.9 | TEST SUITE PERFORMED TO CATEGORIZE TWEETS INTO TWEET TYPE | 58 |
| TABLE 4.10 | TEST SUITE PERFORMED TO DISPLAY DATA VISUALIZATION FOR ANALYSIS CARRIED OUT | 59 |
| TABLE 4.11 | TEST SUITE PERFORMED TO COMPARE TWO KEYWORDS | 62 |
| TABLE 4.12 | TEST SUITE PERFORMED TO EXTRACT INFORMATION OF TWITTER ACCOUNT | 65 |
| TABLE 4.13 | TEST SUITE PERFORMED TO EXTRACT USER TIMELINE | 67 |
| TABLE 4.14 | TEST SUITE PERFORMED TO DISPLAY DATA VISUALIZATION FOR OPTIMIZATION OF POSTS | 68 |
| TABLE 4.15 | TEST TRACEABILITY MATRIX | 73 |
| TABLE 4.16 | TEST REPORT SUMMARY | 75 |
| TABLE 4.17 | ERROR REPORTS AND CORRECTIONS | 75 |
LIST OF FIGURES
FIGURE 2.1 GOOGLE TREND ON SENTIMENT ANALYSIS 8
FIGURE 2.2 SENTIMENT ANALYSIS METHODOLOGY USED IN SENTIMENT ANALYSIS OF NEWS ARTICLES: 11
FIGURE 3.1 PROTOTYPE METHODOLOGY 14
FIGURE 3.2 RAPID APPLICATION DEVELOPMENT MODEL 14
FIGURE 3.3 SCRUM DEVELOPMENT METHODOLOGY MODEL 15
FIGURE 3.4 WATERFALL METHODOLOGY MODEL 16
FIGURE 3.5 SPIRAL MODEL 16
FIGURE 3.6 ITERATIVE INCREMENTAL MODEL 17
FIGURE 3.7.1 MENTION FEEDS ARCHITECTURE 26
FIGURE 3.7.2.1 USE CASE DIAGRAM FOR ONE KEYWORD 27
FIGURE 3.7.2.2 USE CASE FOR COMPARE KEYWORD 28
FIGURE 3.7.3 ACTIVITY DIAGRAM 29
FIGURE 3.7.4.1 CONTEXT LEVEL DIAGRAM 30
FIGURE 3.7.4.4 PROCESS DECOMPOSITION 32
FIGURE 3.7.5 ENTITY RELATIONSHIP DIAGRAM OF MENTION FEEDS 33
FIGURE 3.7.6.2 REGISTER PAGE 34
FIGURE 3.7.6.3 USER KEYWORD DASHBOARD 35
FIGURE 3.7.6.4 ADD NEW KEYWORD 35
FIGURE 3.7.6.5 COMPARE KEYWORDS 36
FIGURE 3.7.6.6 USER SOCIAL MEDIA ACCOUNT DASHBOARD 36
FIGURE 3.7.6.7 ADD NEW TWITTER USERNAME 37
FIGURE 4.1 BOKEH VISUALIZATION 42
FIGURE 4.1.1 TESTING USER REGISTRATION 46
FIGURE 4.1.2 SUCCESSFUL REGISTRATION 46
FIGURE 4.3.1 TESTING LOGIN PAGE 48
FIGURE 4.3.2 LOGIN SUCCESSFUL 48
FIGURE 4.4.1 POP UP TO ADD KEYWORD 49
FIGURE 4.4.2 KEYWORD SUCCESSFULLY ADDED 50
FIGURE 4.5.1 TEST TO ADD NEW KEYWORD 51
FIGURE 4.5.2 TWITTER ACCOUNT SUCCESSFULLY ADDED 52
FIGURE 4.6 TWEETS EXTRACTED SUCCESSFULLY 53
FIGURE 4.7.1 LINE GRAPH FOR EXTRACTED POSTS 55
FIGURE 4.7.2 BAR CHART FOR EXTRACTED POSTS 55
FIGURE 4.8.1 SENTIMENT ANALYSIS VISUALIZATION 60
FIGURE 4.8.2 DEVICE USED VISUALIZATION 60
FIGURE 4.8.3 POST TYPE VISUALIZATION 61
FIGURE 4.9.1 TEST TO COMPARE KEYWORDS 63
FIGURE 4.9.2 COMPARE KEYWORDS DOUBLE LINE GRAPH 63
FIGURE 4.9.3 COMPARE ANALYSIS VISUALIZATION 64
FIGURE 4.9.4 EXTRACT TWITTER ACCOUNT INFORMATION 66
FIGURE 4.9.5 USER TIMELINE EXTRACTED 68
FIGURE 4.9.6 DATA VISUALIZATION FOR POST OPTIMIZATION 69
FIGURE 4.9.7 HISTORIC DATA PAGE 71
FIGURE 4.9.8.1 HISTORIC DATA GRAPH 71
FIGURE 4.9.8.2 HISTORIC DATA SENTIMENT ANALYSIS 72
LIST OF ABBREVIATIONS
| CPU | Central Processing Unit |
| ERD | Entity Relationship Diagram |
| IT | Information Technology |
| API | Application Program Interface |
| HTML | HyperText Mark-up Language |
| CSS | Cascading Style Sheets |
- Overview
CHAPTER 1: INTRODUCTION
The internet is becoming more and more instilled in every human’s day to day activity and life. It has been observed that social media platforms are used consistently 24/7 by users from all around the world to express their opinions on different topics freely.
Many brands and companies take advantage of these platforms to create strategies and understand their target market so as to help in advertising and marketing using influencers on Instagram, twitter, Facebook etc.
Social media monitoring is the process of identifying data relevant to a topic or organization from a social media platform and making various analyses on the data for different purposes such as academic, decision making, marketing strategies etc.
This project will show how social media has and will help businesses in making analysis on the opinions of the customers on their products so as to improve their marketing strategies and know what their target market wants. (Amandeep, Deepesh, Khushboo, & Ranjit Singh, 2016).
Sentiment Analysis is used to classify texts into positive, negative and neutral by using text analysis techniques (Sentiment Analysis, 2020).
Using sentiment analysis to make predictions of emotions in words, sentences or documents, the work can gain an overview of the wider public opinion behind the product. A lot of people use social media sites for networking with other people and news. Social Media provides a platform for people to voice their opinions. For example, a person might have had a very hot day and decided to buy a refreshing bottle of coke and decide to post a picture of themselves on twitter with a caption about it. This kind of information can be used by organizations to evaluate, and rate the performance of their products all around the world as either successful or not. (Khalid & Ahmad, 2017). This is one the many features the project will provide.
Background and Motivation
The previous methods of advertising, marketing and understanding the masses opinions where very time consuming and not always efficient. But social media platforms have provided a cheaper and more efficient way to get organization’s products and services out to the mass. And with this development analysis on how well these products are doing in the market is very possible. A lot of social media platforms provide API’s that give the public access to their data.
TweetReach is a social media tool for twitter specifically. It makes analysis based on keywords, URLs or account names and gives an overview over all the tweets posted.
The advantage of using a social media monitoring tool is that it provides the following:
- Users can have an overview of what people are saying about their products or how many mentions their products are making. It can break down the status of who are making the posts all the way down to their gender, location, source of the post etc.
- The users are getting real-time analysis on their products. Giving data on the number of positive and negative reviews and comments people post on their products. This can help the organization know the next step to take with their marketing strategies (Sentiment Analysis, 2020).
- The user can keep track of their competitors also.
This project is not only limited to organizations or businesses, academic scholars and people in politics can use this project to make analysis in their respective fields too.
These are some of the advantages and features the implementation of this project has to offer to academia, industry and government.
Statement of Problem
In the past companies/organizations/brands had to use radio, television ads, newspaper/print outs etc. to get their products and services to the market. But with the trend of social media it has helped businesses advertise and market their products and services better ways by improving customers’ insights, these businesses can use the platforms to understand what the customers like and dislike on a personal level easily.
It helps to establish brand awareness as most social media platforms have millions of users. This can help the business reach a wide range of customers locally and internationally.
Also, it is very cost effective, if using the previous methods such as using billboards, television ads, newspapers etc. they usually cost a lot and are usually not very cost efficient but using social platforms it costs almost nothing to get your products into the market compared the old ways. (Khanna, 2018).
Also, political candidates also find it difficult have a more accurate data on what the society feels about them and how well their campaign or adverts have affected people’s view on them
The main problem is the accessibility of data for individuals or organizations on their products and analyzing this data to know how successful or unsuccessful the products or strategies are working. This project gives its users access to that information.
Aims and Objectives
Companies, organizations, influencers, political candidates and so many others find it difficult to analyze data or information that is relevant to them on the internet. This thesis as whole aims at providing this data from various social media platforms so as to make analysis and give users an insight of what social media users feel about them or their products/services. Due to the time constraint of this project. This particular project work will focus mainly on twitter.
This project aims at finding the most accurate method of sentiment analysis to help achieve the following:
1.) For brands or keyword:
- Extracts tweets containing the particular keyword requested by the user.
- Find the most efficient method for sentiment analysis on each tweet and then give an average of positive, negative and neutral tweets.
- Show devices that post the tweets
- Show if tweets were original, replies or retweets.
- Compare different brands e.g. iPhone vs Samsung
- Number of engagements in a page
- Sources of posts (e.g. iPhone, Android, Web App e.t.c.)
- Compare two keywords and provides graph shows number of posts for each keyword
- Shows number of followers and following of twitter account
- Shows location of tweets
- Develops graph to give a clearer insight on the analysis being carried out 2.) For Twitter account:
- Shows number of Followers and number of people following
- Types of post by user (i.e. whether reply, original, retweet)
- Shows the top posts with number of likes and retweets
- Shows the amount of engagement of on your page
- Develops a graph that shows the best time to make a post in a day based on the previous day
OBJECTIVE OF THE PROJECT
1.) To find the most accurate method of sentiment analysis
2.) To develop interactive visualization for data analysis done on data
3.) To perform analysis on data gotten either based on keywords or twitter accounts.
These are the aims and objective of this current project as when you get to Chapter 3 you will notice this project is separated into iterations with an enhancement being made at every iteration.
Significance of the Project
The implementation of this project has potential benefits to the academic field and society. In view of society, this research will enhance the marketing strategies used by organizations and companies in Nigeria. It quickly gains insights using large volumes of text data. Product sentiment analysis will provide a platform for companies/brands to understand the opinions of the mass based on their products and advertisements.
It provides some answers into what the most important issues are, from the perspective of customers, at least. Because sentiment analysis can be automated, decisions can be made based on a significant amount of data rather than plain intuition that isn’t always right. (Dumbleton, 2018).
Sentiment analysis also focuses on data science which is a vital branch of computer science. Implementing this project in the country will enhance data management and manipulation, that is, understanding the importance of data and how it can help in business analytics and intelligence as well as many other sectors.
Social media monitoring helps individuals and organizations have access to data available on the internet. It carries out analysis on the data extracted to give a better insight on their topic.
Project Risk Assessment
This section shows possible problems that may occur from the beginning of this project and throughout and suggest possible solutions and prevention techniques that can be used.
Table 1: Risk Assessment
S/N Risk event Risk
Probability
Impact Factors
| Timeframe | (out 100) | of | |||
| Time Efficiency Scope creep | Undefined 20-30 days | 40 out of 100 50 out of | Incomplete Project Incomplete | -Lack of time management -Changes | |
| inflates scope | after project has started | 100 | Project | from lecturers or | |
| supervisors | |||||
| 1.6.3 Dependencies are inaccurate | Undefined | 70 out of 100 | More time or cost | -Lack of proper | |
| spent | planning | ||||
| 1.6.4 Design isn’t feasible | 10-20 days after project has started | 10 out of 100 | Incomplete project | -May be excessively costly |
