P-ISSN: 2827-9832

E-ISSN: 2828-335x

http://ijsr.internationaljournallabs.com/index.php/ijsr

443

TWITTER SENTIMENT ANALYSIS TOWARDS QATAR AS HOST OF

THE 2022 WORLD CUP USING TEXTBLOB

Syarafina Dewi, Dede Brahma Arianto

Satya Wacana Christian University, Islamic University of Indonesia

672019063@student.uksw.edu, 18917109@students.uii.ac.id

ABSTRACT

Twitter provides services to its users in the form of creating status messages or what is usually called tweets.

Through tweets, users can express opinions, views, or emotions toward a topic. On 2 December 2010, Qatar

was selected to host the 2022 World Cup. Qatar's selection as the host of the 2022 World Cup could elicit a

variety of responses from various circles around the world. This research used TextBlob to find out the

sentiment of Twitter users around the world regarding Qatar as the host of the 2022 World Cup. The

research uses three stages, the first stage before Qatar was selected to host there was 88.46% positive

sentiment and 11.54% negative sentiment, the second stage after Qatar was selected to host there was

79.38% positive sentiment and 20.62% negative sentiment, the third sentiment when the 2022 World Cup

took place in Qatar was 83.72% positive sentiment and 16.28% negative sentiment. Based on the study, an

accuracy score of 83% was obtained, meaning that the model was able to accurately predict 83% of the total

testing data. This study can predict new data without having to be labeled first.

Keywords: Qatar, FIFA World Cup 2022, sentiment analysis, TextBlob

This article is licensed under CC BY-SA 4.0

INTRODUCTION

Twitter is one of the most popular social communication platforms today. Twitter can

connect people around the world through computers or mobile phones. Twitter provides

services to its users in the form of creating status messages or what is usually called tweets

(Fikri et al., 2020). Through tweets, users can express opinions, views, or emotions towards a

topic, if the topic gets a lot of responses accompanied by the use of hashtags by users it will

become a trending topic.

One of the topics that echo on Twitter and is a trending topic right now is the 2022 FIFA

World Cup. On 2 December 2010, Qatar surprisingly won the bid as host of the 2022 FIFA

World Cup, thus Qatar becoming the first Middle Eastern country to host the World Cup. Qatar

was chosen to host after defeating Australia, Japan, South Korea, and the United States who

are also running to host the 2022 World Cup. Qatar's selection as the host of the 2022 World

Cup could elicit a variety of responses from various circles around the world. Social media

such as Twitter is one of the places to give a response or opinion. (Odd, 2016) This can be used

as material for sentiment analysis toward Qatar as the host of the 2022 World Cup.

Sentiment analysis belongs to one of the areas of Natural Language Processing (NLP) and

is a process designed to identify the content of datasets in the form of opinions or views

(sentiments) in the form of text on topics that are positive, negative, or neutral. However, users

will face difficulties when reading Fauzi & Adinugroho (2018) tweets directly without

marking them as positive, negative, or neutral. Therefore, a classification is needed that allows

users to easily see which tweets are of positive or negative value (Primary, Ariesta, & Gata,

2022).

Twitter Sentiment Analysis Towards Qatar as Host of the 2022 World Cup Using TextBlob

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Research conducted by Ravikumar Patel and Kalpdrum Passi entitled "Sentiment Analysis

on Twitter Data of World Cup Soccer Tournament Using Machine Learning" explains that the

analysis was carried out by applying machine learning techniques. The data collected in this

study is tweet data with hashtags "#brazil2014", "#worldcup2014", and match hastags. Results

showed that Naïve Bayes provided the best accuracy of 88.17%, while random forest provided

the best area with an AUC of 0.9 (Patel & Passi, 2020).

Research conducted by I Gede Susrama Mas Diyasa et al with the title "Twitter Sentiment

Analysis as an Evaluation and Service Base on Python Textblob" explained that Twitter can be

a place to express feelings, to customers, one of which is PT Telkom Indonesia. Tweet data

processed using Textblob resulted in 34.4% positive tweets, 16.1% negative tweets, and 49.6%

neutral tweets (Mas Diyasa et al., 2021).

METHOD

This study used the flowchart methodology in Figure 1.

Figure 1. Research methodology flowchart

The data that has been collected cannot be directly analyzed because the data is still not

clean. Data that has not been cleaned must go through several stages of Data Preprocessing.

Once the data is clean, it will be analyzed using TextBlob to find out which tweets are positive

and negative. The final step is the visualization of the results of the analysis.

1. Data Collection

Tweet data is captured by means of web scraping using SNScrape. SNScrape is a python

library that is useful for web scraping on social media, one of which is Twitter. Researchers

took a Rivaldi et al (2022) tweet in English relating to the World Cup in Qatar in 2022. Data is

collected starting before Qatar was selected to host, after being selected to host, and when the

2022 World Cup took place in Qatar.

2. Data Preprocessing

Data Preparation is performed before the dataset is implemented into the model. Data

Preparation is a must. The goal is to reduce data noise to make it cleaner so that maximum

results are obtained. The process is divided into several stages, namely: (1) drop unnecessary

columns to remove unnecessary columns, (2) change column name to change column names

Twitter Sentiment Analysis Towards Qatar as Host of the 2022 World Cup Using TextBlob

445 2, 2., Januari 2023

to make it easier to understand, (3) add tweet columns to add new columns that later the data

has been cleaned, (4) remove hyperlinks to remove links in tweets, (5) remove retweets to

remove the retweet label in text, (6) remove symbols to remove symbols in the tweet, (7)

remove stopwords to remove words that have a function but have no meaning, (8) remove

duplicate to delete duplicate data (Giovani et al., 2020).

3. Data Classified

The sentiment analysis process is performed using the TextBlob library. TextBlob is a

library used for textual information that provides a simple API for accessing Neuro-Linguistic

Programming (NLP) activities. (Hazarika et al., 2020)The TextBlob library is capable of

processing three types of classifications, namely positive, negative, and neutral. However,

TextBlob can only be done in English. Therefore, researchers only take tweets that use English.

In TextBlob there is a calculation that returns the polarity value. If the polarity value is greater

than 0, then it is positive. If the polarity value is equal to 0, it is neutral. If the polarity value is

less than 0, then negative (Mas Diyasa et al., 2021).

4. Tokenization

Tokenization is the process of breaking a sentence into a stand-alone set of words.

Tokenization breaks down text that was originally a sentence into words. Tonization also

eliminates delimiters such as periods, commas, spaces, and number characters in sentences

(Hafidz, 2020).

5. Data Visualization

The visualization of the sentiment analysis results is displayed with the matplotlib library

which is one of the libraries in Python for performing statistical data processing, visualization,

and plotting (LEMENKOVA, 2019). The visualization is displayed in the form of a word

cloud. A Word cloud is a display of words that appear frequently, and its size shows the

frequency of occurrence in the data. Words whose frequency of occurrence is larger in size.

On the contrary, words whose frequency of occurrence is less are smaller in size (Permadi,

2020).

RESULTS AND DISCUSSION

1. Data Collection

The web scraping process is carried out in three stages, the first stage was when Qatar

offered to host in March 2009 until before the announcement of Qatar's selection to host on

December 1, 2010, the second stage which was after the official announcement of Qatar's

selection to host on December 2, 2010, until one year later on December 2, 2011, and the last

stage is when the FIFA World Cup starts on November 20, 2022 until December 18, 2022.

Table 1. Twitter Data Descriptions

Keywords

Date

Sum

Qatar 2022 World Cup

March 1, 2009 - December 1, 2010

3606

Qatar 2022 World Cup

December 2, 2010 - November 19, 2022

41225

Qatar 2022 World Cup

November 20, 2022 - December 18, 2022

200001

Table 1 is the date the tweet was retrieved with the amount of data on that date. The

maximum data results from web scraping are up to 200,000 at each stage, this is done so that

the data is not unlimited.

Twitter Sentiment Analysis Towards Qatar as Host of the 2022 World Cup Using TextBlob

446 2, 2., Januari 2023

Table 2. Sample Tweet Data

Datetime

Tweet Id

Text

Username

2010-11-30

9.72454E+15

Handicapping the 2022 World Cup

Candidates - Qatar – First of all,

Qatar is HOT in the summer, like

kill... http://tumblr.com/xfzxxnsrs

jeff_underscore

2010-11-30

9.72392E+15

Aussies 2022 World Cup bid takes

a hit report ranks Aus last in revenue

USA scored 100%, AUS 68%,

Japan 73%, Sth Korea 71%, Qatar

70%

meredith_gibbs

2010-11-30

9.72213E+15

Branding The #Qatar World Cup

2022 Bid MT @RobaAssi:

http://bit.ly/i9QfTS via

@natashaTynes

octavianasr

2010-11-30

9.71964E+15

Qatar 2022 World Cup Bid Gets

Boost from Facebook – Mediabistro

... http://bit.ly/fOn8VA #worldcup

DC_Cappers

2010-11-30

9.71433E+15

Spector scored those two goals in

order to get back at Sir Alex

Ferguson backing Qatar's 2022

World Cup Bid. #goUSAbid

ChrisThomasFC

In this study, researchers used the attributes 'Datetime', 'Tweet Id', 'Text', and 'Username'

taken from the web scraping process. An example sample of web scraping result data is shown

in Table 2. After the data is collected, the tweet frequency can be seen in Figure 2, Figure 3,

and Figure 4.

Figure 2. Tweet Frequency Before Qatar's Vote to Host

The diagram in Figure 2 shows the frequency of tweets from the time Qatar was nominated

to host the 2022 FIFA World Cup in March 2009 until before the announcement Qatar was

officially selected to host the 2022 FIFA World Cup on December 1, 2010. It can be seen that

after March 2010 there was a rapid increase in the number of tweets although there was a

decrease, but after that the number of tweets rose again.

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Figure 3. Tweet Frequency After Qatar Was Selected to Host

The diagram in Figure 3 shows the frequency of tweets after Qatar was selected to host the

2022 FIFA World Cup on December 2, 2010, to one year later on December 2, 2011. It can be

seen that the number of tweets is the most right after the announcement, there is a sharp decline

in January 2011, then the chart tends to stabilize.

Figure 4. Tweet Frequency during the 2022 FIFA World Cup

The diagram in Figure 4 shows the frequency of tweets at the start of the 2022 FIFA World

Cup in Qatar from November 20, 2022, to December 18, 2022. It can be seen that the peak of

tweets occurred at the opening of the World Cup, after that there was a decline. Even though

the number of tweets had increased quite drastically on December 9 to 10 2022, after that it

went down again.

2. Data Preprocessing

Table 2 contains sample data, the 'Original Tweet' column shows tweets that still contain

unnecessary URLs, usernames, and ASCII symbols that could interfere with the visualization.

Therefore, it must be removed first. The data that has not been cleaned is carried out in several

processes that have been described in the research methods section. After several stages of data

preprocessing, the data will be easier to process into information. The clean data can be seen

in Table 3 of the 'Tweets' column.

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Table 3. Sample Data Before Cleaning and After Cleaning

Original Tweet

Tweets

Aussies 2022 World Cup bid takes a hit report

ranks Aus last in revenue USA scored 100%,

AUS 68%, Japan 73%, Sth Korea 71%, Qatar

70%

Aussies World Cup Bid takes Hit

Report Ranks as last revenue USA

scored as Japan ST Korea Qatar

Branding The #Qatar World Cup 2022 Bid MT

@RobaAssi: http://bit.ly/i9QfTS via

@natashaTynes

branding Qatar world cup bid mt via

Qatar bidding nation FIFA world cup 2022

Qatar bidding nation FIFA world cup

Let's all support #qatar and its world cup 2022 bid

http://yfrog.com/47jlp01j

Let Us Support Qatar World Cup Bid

Is it really smart for USA fans to make fun of the

Qatar 2022 World Cup bid based on their human

rights record? #JustSaying

really smart USA fans make fun Qatar

world cup bid based human rights

record just saying

3. Data Classified

The data structure that used in this process is data that already through the data

preprocessing process. Tweet text data consist of predictor variable that is tweets that already

cleaned, and response variable contains the tweet sentiment of the classification results

(positive and negative). Next, code to implementation TextBlob.

The sentiment result of ‘1’ is positive, while the result of sentiment of ‘-1’ is negative. If the

negative score exceeds the positive score, then the tweet is included in negative sentiment,

besides that it is included in positive sentiment. This can be seen in the code. Table 4 is a

sample of the results of the analytical sentiment used in this study.

#Use textblob's sentiment method to analyze sentiment of passed tweet

after_tweets_df[['Polarity', 'Subjectivity']] = after_tweets_df['Tweet'].apply(Lambda Text: pd. Series(TextBlob(Text).sentiment))

#Calculating Negative, Positive and Compound values

for index, row in after_tweets_df'Tweet'].iteritems():

score = SentimentIntensityAnalyzer().polarity_scores(row)

neg = score['neg']

post = score['post']

comp = score['compound']

if neg > post:

after_tweets_df.loc[index,'Sentiment'] = "-1"

else:

after_tweets_df.loc[index,'Sentiment'] = "1"

after_tweets_df.loc[index,'neg'] = neg

after_tweets_df.loc[index,'post'] = post

after_tweets_df.loc[index,'compound'] = comp

Twitter Sentiment Analysis Towards Qatar as Host of the 2022 World Cup Using TextBlob

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Table 4. Sample Sentiment Analysis Results

Original Tweets

Tweets

Polarity

Subje

ctivity

Senti

ment

Neg

post

compo

und

Let's all support #qatar

and its world cup 2022

bid

http://yfrog.com/47jlp01j

Let us support

qatar world cup

bid

0.31

0.4

@kyle_newman I assume

de Qatar sheiks will bribe

more than the US for the

2022 World Cup. Besides

in that region there was no

WC before.

Assume de Qatar

Sheiks Bribe US

World Cup

besides Region

-1

0.15

-0.2

Excited for the world cup

bid results, I wish, from

the bottom of my heart

that Qatar will host it in

2022 #Qatar2022

Excited World

Cup Bid Results

Wish Bottom

Heart Qatar Host

Qatar

0.375

0.75

0.36

0.62

FIFA Qatar Eyes 2022

World Cup

http://tinyurl.com/3x7tbn

fifa qatar eyes

world cup

Mark it: Russia for World

Cup 2018, Qatar for

World Cup 2022. Nothing

surprises me with FIFA

anymore. Lack of FIFA

talk for US bid is odd.

mark russia

world cup qatar

world cup

nothing

surprises fifa

anymore lack

fifa talk us bid

odd

-0.1667

0.25

-1

0.31

-0.64

Figure 5. Percentage of Sentiment Before Qatar's Election to Host

The classified data process is divided into three stages, the first stage being when Qatar

offered to host in March 2009 until before the announcement of Qatar's selection to host on

December 1, 2010. Figure 5 is the result of the sentiment analysis produced in the first stage.

Before Qatar was announced as the host, it showed that people had little negative feelings

towards Qatar. In contrast, positive sentiment resulted is more than 88% of the total data

retrieved. The number of tweets before Qatar was announced as the host was also very small

compared to after the announcement and at the time Qatar's 2022 World Cup took place.

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Figure 6. Comparison of Positive and Negative Sentiment before the Announcement

In Figure 6 the Y axis represents the number of tweets. Meanwhile, the X axis represents

each month. The line chart compares negative sentiment and positive sentiment each month.

Figure 7. Percentage of Sentiment After Qatar's Election to Host

The second stage was after the official announcement of Qatar's election to host on

December 2, 2010 until one year later on December 2, 2011. Figure 7 is the result of the

sentiment analysis produced in the second stage. After Qatar was announced as the host, it

shows that people have an increase in negative sentiment toward Qatar. Meanwhile, positive

sentiment has decreased slightly from before. When viewed from the number of tweets after

Qatar was announced as the host has increased a lot compared to before Qatar was announced

as the host. The increase in the number of tweets was followed by an increase in negative

sentiment towards Qatar.

Figure 8. Comparison of Positive and Negative Sentiment after the Announcement

In Figure 8 the Y axis represents the number of tweets. Meanwhile, the X axis represents

the number of tweets in each month. The line chart compares negative sentiment and positive

Twitter Sentiment Analysis Towards Qatar as Host of the 2022 World Cup Using TextBlob

451 2, 2., Januari 2023

sentiment each month. The increase in positive sentiment was accompanied by an increase in

negative sentiment.

Figure 9. Sentiment Percentages during the 2022 FIFA World Cup

The third stage is when the 2022 FIFA World Cup in Qatar starts on November 20, 2022,

until December 18, 2022. Figure 9 is the result of the sentiment analysis produced in the third

stage. During the 2022 FIFA World Cup in Qatar, people have decreased negative sentiment

towards the 2022 World Cup in Qatar. Otherwise, positive sentiment has increased than after

Qatar was announced as the host. When viewed from the number of tweets during the 2022

FIFA World Cup in Qatar has increased dramatically than after Qatar was announced as the

host.

Figure 10. Comparison of Positive and Negative Sentiment during the 2022 World Cup

In Figure 10 the Y axis represents the number of tweets. Meanwhile, the X axis represents

each day. The line chart compares negative sentiment and positive sentiment each day. The

increase in positive sentiment was accompanied by an increase in negative sentiment.

4. Tokenization

The tokenization process is used to recognize words and break sentences into words based

on spaces and punctuation. Table 4 is a sample of the tokenization results.

Table 5. Tokenization Process

Tweets

branding qatar world cup bid mt

via

'branding', 'qatar', 'world',

'cup', 'bid', 'mt', 'via'

qatar bidding nation fifa world

cup

'qatar', 'bidding', 'nation',

'fifa', 'world', 'cup'

Let Us Support Qatar World Cup

Bid

'let', 'us', 'support', 'qatar',

'world', 'cup', 'bid'

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5. Data Visualization

The data that has gone through the tokenization process is then grouped to get the frequency

of the words that appear the most. The frequency of this word will be visualized into a positive

and negative wordcloud as shown in Figure 11, Figure 12, and Figure 13.

Figure 11. Positive and Negative Wordcloud Before Qatar Was Selected to Host

Figure 12. Positive and Negative Wordcloud After Qatar Was Selected as Host

Figure 13. Wordcloud Positives and Negatives when the 2022 World Cup takes place in

Qatar

6. Confusion Matrix Calculation

Confusion matrix is a method used to measure the performance of a model. The Confusion

Matrix contains information that compares the results of the classification made by the model

with the actual results. There are four terms to represent the results of the classification process,

namely True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).

A True Positive value is the amount of positive data that is correctly classified by the model.

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A True Negative value is the amount of negative data that the model correctly classifies. The

value of False Positive is the sum of positive data that are misclassified by the model. A False

Negative value is the amount of negative data that are misclassified by the model (Setiawan et

al., 2020).

Figure 14. Confusion Matrix Results

This research performs calculations that include accuracy and precision. The accuracy value

indicates how accurately the system can correctly classify the data. The precision value

indicates the amount of data correctly classified as positive, divided by the total positive data.

The results of the confusion matrix are shown in Figure 14. The test result of 80% of the

training data and 20% of the overall testing data. TextBlob sentiment analysis yielded an

accuracy of 83.35%.

CONCLUSION

In this study, sentiment analysis of tweets towards the 2022 FIFA World Cup in Qatar

has been carried out using TextBlob. Data obtained from Twitter through web scraping with

the keyword search for the 2022 FIFA World Cup in Qatar, obtained data as many as 244,832

tweets. The data is then analyzed to determine positive and negative sentiment. The research

uses three stages, the first stage before Qatar was selected to host there was 88.46% positive

sentiment and 11.54% negative sentiment, the second stage after Qatar was selected to host

there was 79.38% positive sentiment and 20.62% negative sentiment, the third sentiment when

the 2022 World Cup took place in Qatar was 83.72% positive sentiment and 16.28% negative

sentiment. Based on the study, an accuracy score of 83% was obtained, meaning that the model

was able to accurately predict 83% of the total testing data. This study can predict new data

without having to be labeled first.

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