Site icon Premium Researchers

A DYNAMIC MODEL OF SOCIAL MEDIA MONITORING TOOLS WITH SENTIMENT ANALYSIS

Do You Have New or Fresh Topic? Send Us Your Topic


Post Views:
1

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                                                                                                                                                                      1BACKGROUND AND MOTIVATION                                                                                                                       1STATEMENT OF THE PROBLEM                                                                                                                                        2AIM AND OBJECTIVES                                                                                                                                            3SIGNIFICANCE OF THE PROJECT                                                                                                                                    4PROJECT RISKS ASSESSMENT                                                                                                                                       5SCOPE/PROJECT ORGANIZATION                                                                                                                                  7

CHAPTER 2: LITERATURE REVIEW                                                                                        8

INTRODUCTION                                                                                                                                                         8HISTORICAL OVERVIEW                                                                                                                                                 8RELATED WORK                                                                                                                                                            9SUMMARY                                                                                                                                                                13

CHAPTER 3: REQUIREMENTS ANALYSIS AND DESIGN                                                      14

OVERVIEW                                                                                                                                                                   14PROPOSED METHODOLOGY                                                                                                                                         14APPROACH TO CHOSEN METHODOLOGY/METHODS                                                                                                   19TOOLS AND TECHNIQUES                                                                                                                                            21ETHICAL CONSIDERATION                                                                                                                                           22REQUIREMENT ANALYSIS                                                                                                                                            23REQUIREMENTS SPECIFICATIONS                                                                                                                               24Functional Requirement Specifications                                                                          24Non-Functional Requirement Specifications                                                                  26SYSTEM DESIGN                                                                                                                                                          26Application Architecture                                                                                             26Use Case                                                                                                                        27Activity Diagrams                                                                                                          30Data Flow Diagram                                                                                                       31Entity-Relationship Diagram (ERD)                                                                               34User Interface Design                                                                                                    35Summary                                                                                                                      39

CHAPTER 4:  IMPLEMENTATION AND TESTING                                                                 40

4.1        OVERVIEW404.2        MAIN FEATURES404.3        IMPLEMENTATION PROBLEMS434.4        OVERCOMING IMPLEMENTATION PROBLEMS444.5        TESTING44CHTests 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                                                                                      76USE GUIDE                                                                                                                                                                  77SUMMARY                                                                                                                                                                77

CHAPTER 5: DISCUSSION, CONCLUSION, AND RECOMMENDATIONS                          78

OVERVIEW                                                                                                                                                                   78OBJECTIVE ASSESSMENT                                                                                                                                            78LIMITATIONS AND CHALLENGES                                                                                                                                   78FUTURE ENHANCEMENTS                                                                                                                                           78RECOMMENDATIONS                                                                                                                                           79CONCLUSION                                                                                                                                                               80SUMMARY                                                                                                                                                                80

REFERENCES                                                                                                                               81

APPENDICES                                                                                                                                83

LIST OF TABLES

TABLE 1RISK ASSESSMENT5TABLE 2SENTIMENT ANALYSIS TOOLS/TECHNIQUES12TABLE 3.1BRAINSTORMING REPORT20TABLE 3.2FUNCTIONAL REQUIREMENT SPECIFICATIONS23TABLE 3.3NON-FUNCTIONAL REQUIREMENT SPECIFICATIONS25TABLE 4.1TEST SUITE PERFORMED FOR REGISTER45TABLE 4.2TEST SUITE PERFORMED FOR LOGIN47TABLE 4.3TEST SUITE PERFORMED TO ADD KEYWORD TO USERS’ DASHBOARD48TABLE 4.4TEST SUITE PERFORMED TO ADD TWITTER ACCOUNT TO USERS’ DASHBOARD50TABLE 4.5TEST SUITE PERFORMED TO EXTRACT LATEST 100 TWEETS FROM TWITTER BASED ON KEYWORD52TABLE 4.6TEST SUITE PERFORMED TO DISPLAY DATA VISUALIZATION FOR NUMBER OF POSTS PER DAY54TABLE 4.7TEST SUITE PERFORMED TO PERFORM SENTIMENT ANALYSIS OF POST EXTRACTED56TABLE 4.8TEST SUITE PERFORMED TO CATEGORIZE TWEETS INTO TYPE OF DEVICE USED57TABLE 4.9TEST SUITE PERFORMED TO CATEGORIZE TWEETS INTO TWEET TYPE58TABLE 4.10TEST SUITE PERFORMED TO DISPLAY DATA VISUALIZATION FOR ANALYSIS CARRIED OUT59TABLE 4.11TEST SUITE PERFORMED TO COMPARE TWO KEYWORDS62TABLE 4.12TEST SUITE PERFORMED TO EXTRACT INFORMATION OF TWITTER ACCOUNT65TABLE 4.13TEST SUITE PERFORMED TO EXTRACT USER TIMELINE67TABLE 4.14TEST SUITE PERFORMED TO DISPLAY DATA VISUALIZATION FOR OPTIMIZATION OF POSTS68TABLE 4.15TEST TRACEABILITY MATRIX73TABLE 4.16TEST REPORT SUMMARY75TABLE 4.17ERROR REPORTS AND CORRECTIONS75

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 3.7.4.2 LEVEL 0 DFD                                                                                                                                                       30

FIGURE 3.7.4.3 LEVEL 1 DFD                                                                                                                                                      31

FIGURE 3.7.4.4 PROCESS DECOMPOSITION                                                                                                                                32

FIGURE 3.7.5    ENTITY RELATIONSHIP DIAGRAM OF MENTION FEEDS                                                                              33

FIGURE 3.7.6.1 LOGIN PAGE                                                                                                                                                         34

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 ABBREVIATIONSCPUCentral Processing UnitERDEntity Relationship DiagramITInformation TechnologyAPIApplication Program InterfaceHTMLHyperText Mark-up LanguageCSSCascading Style Sheets

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 tweetsShow if tweets were original, replies or retweets.Compare different brands e.g. iPhone vs SamsungNumber of engagements in a pageSources of posts (e.g. iPhone, Android, Web App e.t.c.)Compare two keywords and provides graph shows number of posts for each keywordShows number of followers and following of twitter accountShows location of tweetsDevelops graph to give a clearer insight on the analysis being carried out 2.) For Twitter account:Shows number of Followers and number of people followingTypes of post by user (i.e. whether reply, original, retweet)Shows the top posts with number of likes and retweetsShows the amount of engagement of on your pageDevelops a graph that shows the best time to make a post in a day based on the previous dayOBJECTIVE 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 creepUndefined       20-30 days40 out of 100     50 out of Incomplete Project   Incomplete-Lack of time management -Changesinflates scopeafter project has started100 Projectfrom lecturers or     supervisors1.6.3    Dependencies are inaccurateUndefined70 out of 100 More time or cost-Lack of proper    spentplanning1.6.4    Design isn’t feasible10-20 days after project has started10 out of 100 Incomplete project-May be excessively costly

Related

Not What You Were Looking For? Send Us Your Topic

INSTRUCTIONS AFTER PAYMENT

After making payment, kindly send the following:

» Send the above details to our email; contact@premiumresearchers.com or to our support phone number; (+234) 0813 2546 417 . As soon as details are sent and payment is confirmed, your project will be delivered to you within minutes.