The impact of sentiment analysis from user on Facebook to enhanced the service quality

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 4, August 2021, pp. 3424~3433
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i4.pp3424-3433  3424
Journal homepage: http://ijece.iaescore.com
The impact of sentiment analysis from user on Facebook to
enhanced the service quality
Daniel D. Albesta, Michael L. Jonathan, Muhammad Jawad, Oktovianus Hardiawan,
Derwin Suhartono
Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia
Article Info ABSTRACT
Article history:
Received Sep 8, 2020
Revised Dec 22, 2020
Accepted Jan 19, 2021
Facebook's influence on the modern social media platform is undoubtedly
enormous. While it has gotten a backlash for its inability to control its
influence over important affairs, there are still many questions regarding
people's perception of Facebook and their sentiment over Facebook. This
paper's role in this ongoing debate is to give a glimpse of people's sentiment
and perception of Facebook in recent times. By collecting samples data from
Facebook's Top Page, this paper hopes to represent a significant amount of
people's aspirations towards this company. By processing the data with a
processing tool to construct and model out the data and a sentiment analyzer
tool helps determine the sentiment, this paper can deduce a 600-comment
worth of processed data. The results from the 600 sampled comments
concluded that the sentiments towards Facebook are 41.50% negative
comments, 22.83% neutral comments, and 35.67% positive comments.
Keywords:
Data mining
Facebook
Sentiment analysis
Service quality
This is an open access article under the CC BY-SA license.
Corresponding Author:
Derwin Suhartono
Computer Science Department, School of Computer Science
Bina Nusantara University
K. H. Syahdan 9 street, Palmerah, 11480, West Jakarta, Indonesia
Email: dsuhartono@binus.edu
1. INTRODUCTION
Sentiment analysis in [1] is a text-based data quarrying method. Applied in an array of chars implied
ASCII code to procure core aim: To tag, extract, and bind in-depth subjectively information of a set of the
source of hid hint on info, specifically in a snippet of digital information. Millions of information currently
stream out from the online social communication application such as Facebook as a part of big data
complicity. Based on the research fact, up to December 2019, Facebook has been hitting up to billions of
monthly active accounts, and it would keep rising in 2020 and beyond. Based on [2], over half a million
comments and almost one-third of million of posts exist newly inside it regularly. Mostly, all the conveyed
data inserted by the user modeled as a compilation of an interpretation of information called opinions. An
opinion needed to be set-based processed, in view, it would be enhanced the analyzing progress for purposes,
but the authors' field pointed to the business concern of Facebook. Enormous opinions produced each minute
on the Facebook scoop may have been influencing the development of Facebook company.
An Opinion can be derived as explicitly or implicitly a sense form of satire. Both senses have a
consequent on the development of the company, can be disastrously or triumphantly determined by the
effectiveness of the opinion itself, so the point of the problem occurred is what the consequences of a user
opinion on the Facebook platform to the Facebook service quality itself, are they would make the service be
worsening or bettering? An efficient method can act as a delimiter on a view, e.g., sentiment and intent
analysis. Sentiment analysis is dealing with explicit information and denotes the highlight object to be

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The impact of sentiment analysis from user on Facebook to… (Daniel D. Albesta)
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determined if it is classified as the positive, neutral, or maybe negative sentiment. The concept of intent
analysis veiled inside [3] takes a whole new level in dealing with analysis data by doing analytics procedures
applied in diverse user's messages to determine whether it has any intention chemistry associates with
positive or negative argumentation. As mentioned before the application of both analysis type have the
potential to perform huge consequential for the company (Facebook) itself, e.g., As the source taken from
[4]. As it implicitly tells us that Facebook is at the position of the lowest bound of the visualization of the
graphic chart provided of user satisfaction of Facebook services, which means that the service as the primer
factor does not fulfill the qualified percentage needed for the user satisfaction rate. It gives rise to the user's
opinion to be bad enough for the company; at this instant, the entanglement of sentiment analysis on millions
of data evolves into a primary essential task to handle corrupted opinion for the sake of Facebook's future
development. A simple but comprehensive method is needed as a delimiter desire of opinion by utilizing the
sentiment analysis method. This research elaborates on the analytical of sentiment objects implied within
Facebook's essential elements such as posts and commentary about Facebook services as a source of
Sentiment data as it is represented in the [5].
2. RELATED WORKS
Liu [6] defines the sentiment analysis as the computational study of opinions, sentiments, and
emotions that are communicated through the text medium. While Balahur and Turchi [7] defines it as the task
of detecting, extracting, and classifying opinions and sentiments concerning different topics, as expressed in
textual input. The desires and personal attractiveness of customers stem from an emotion that arises either
positively or negatively in which it provides an essential factor in determining a decision as in [8] if the
content of the user comment gets detail, which means more abstract less, would be produced more emotions
on it, based on the evidence occurred. As stated in [9], for some cases, there is a time when the candidates-
people communicate channel utilized Facebook as their other communication media in Mexico. Even though
social media is majorly used as part of the political campaign, in this era, the implementation of the sentiment
analysis has no chance to predict the result of an election.
Although the analytical using the sentimental of a user has a significant impact on the latest
developments, this would not happen if it does not have a method that is efficient in the process. The model
is as mentioned in [10]; the authors used an action-object approach for post-classification to understand
social media marketing, implied within such as the sentimentally of a user on a Facebook brand page by
following some of the coding development strategies which consist of two steps, tagging, and integrating.
The tagging objective is to identify and then split up different keywords on a Facebook post or comments.
While integration is the part where they classify the tagging results that the authors concluded were that to
run a successful Facebook page, the marketing team must be more concerned about what things commonly
shared. Although the existing data processing can works with other tools as in [11], the authors are trying to
achieve how to use open source technology with Hadoop based utilization to capture inputs about a brand's
information and feedback through Facebook posts and comments. They outlined six steps to acquire the data,
and it was already tested with six different brands. The accuracy of sentiment predictions of their testing
ranges from 53.33% up to 76.76%. As the analytical using the sentimental of a user, based on a process by
expressing the user opinions in a file, following that, it would continue to another process, which is the
identification and categorization, these aims to predict of the writer's attitude, whether it is a lively, neutral,
or maybe negative sentiment. By doing the analytical process, it would be easy to recognize whether the post
contains any sentimental polarity value, also to know of what most discussed topic, it is likely to apply the
K-means clustering method within it. Also, the analytical process by using a user sentiment is often used to
evaluate the content within the social media platform and to forecast any emotion that occurs within the
consumer for incidents that are observed on a full scale in real-time [12]. Although sentiment analysis is also
used to predict personality based on users behaviour [13].
Sentiment analysis does have a significant impact today. They can comprehend other personally
from their behavior [14]. From previous research, they implied an approach within the analysis with a lexicon
algorithm. The fundamental theory within this approach rests on the idea, which is to comprehend the
concept of lexical itself and generate some lexical sentences as pieces as an essential part of learning a
language. The sentiment analysis and Lexicon based dictionary method have shared an effective rate of effort
in such kinds of works, which produces a lot of functional benefits [15]. Moreover, one method to find the
personality traits of the user is to read a text and to understand them. Also, the same for [16], which using
Facebook because it explains which is exceedingly known well all over the world, these platforms hold a
vital role in personality analysis based on users' activities. Because the concept of personality is a little bit
complicated, it would presently be an issue known to be challenging to be solved. As the issue which cannot
be easy to be solved, documentation comprehension can be puzzled out by using the supervised nor

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unsupervised method of the machine learning algorithm [17]. Natural language processing is one of them
which implemented in [18]. It is commonly used for a simple way to implement sentiment analysis when
there is a small chance that learning syntax would be necessary. Based on the previous papers that discussed
the various applications of sentiment analysis on multiple different subjects, all of them have somewhat the
same step by step that researchers generally used when researching this subject. The implementation of
sentiment analysis varies from its usage to predict personality modelling gathered from Facebook [19], to
exploring its usage on Arabic slang [20], to its application in e-learning [21], and more. The other approach is
to use SentiCircle and Lexicon-Based algorithms as in [22] implementing sentiment analysis in semantic
contextual in the Twitter platform in 2016, which is supported by the Semicircle workflow model. To make
the algorithm much better processing.
Besides, there is a way to improvise against sentiment analysis as in [23] with the sentence type
classification with the implementation of the BiLSTM-CRE and convolutional neural network (CNN)
algorithm in 2017. It has a variety of algorithms to help break down problems such as co-ranking, back-
propagation, and other algorithms that share the way it works. There are still many other ways of using
sentiment analysis as [24] there is a unique method of utilizing the hybrid cuckoo search method in the year
2017. Not only by implementing algorithms such as K-mean, cuckoo search, and other algorithms that
resemblance to it, the implementation of architecture models such as the hybrid cuckoo search method
flowchart model. There is also another way in the application of the sentiment analysis that is with [25] the
implementation of general knowledge (commonsense knowledge) into an attentive long short-term memory
(LSTM) in 2018, which has an algorithm of deep neural network and has its architectural model for semantic
network. Admittedly, there are supporting methods to implement the applied algorithm that is the
implementation of the method: Target-level attention, Sentic LSTM, and other similar methods.
Based on the preceding researched, mixed methods and appliances approached have been done and
consummated; all of them have appropriateness and applicability to be applied to various errands. From
previous research, sentiment analysis commonly exists on the Feedback activity in the scope of service apps
(e.g., Facebook). Most of Facebook's users have communicational interacted through the news posts that
have been posted by typing their thoughts of the comment section instead rather than doing it with personal
chat. From the previous studies, one of the social media platforms used to perform text processing was
Facebook. Furthermore, it explains that any automatically generated content application has a feature that is
capable of being as guidance for deducing the character behavior of the user.
3. RESEARCH METHOD
The method of working can be composed of three primer objectives, which divide into variants of
prominent diverge segmentations-the dataset collection segment, the data pre-processing segment, and the
analyzed data visualization segment. To operate the algorithms of the methodology, as utilized the Python
integrated development environment (Python IDE ver.3.7.7.) as the base of the programming language to
conduct the experiments with the Python package index (PIP) as the repository of the software. Besides that,
the method of working helped by with the assistance of web-based application such as open-document format
based on Jupyter-notebook for Windows platform (JSON), text-analysis and machine learning web-based
service (MonkeyLearn on MonkeyLearn.com), Facebook data comment's extractors via web-based services,
and Microsoft Excel 2016 as the data container. For the information, it applied the method of working based
on the existing methods from the internet, the method [26] applied based on a GitHub Post.
The aimed method of working at the three selectively Facebook's posts comments:
 1st
Post, Facebook Official Page-https://www.Facebook.com/Facebook/videos/190403452280238/
 2nd
Post, Mark Zuckerberg Official Page-https://www.Facebook.com/zuck/posts/10111707373631191/
 3rd
Post, Mark Zuckerberg Official Page-https://www.Facebook.com/zuck/posts/10111462102202101/
Which all of them acted as clarity of data resources. As it analyzed, it derived a much potential array
of the raw data from it as the primary sources, which are loaded by approximately 600 comments combined
as the sample raw data to be analyzed furthermore.
3.1. Data collection segment
As it used an online algorithm of Python that can generate the sentiment graph from a comment
scrapper website (like DataExtractor.io), which scrapped the user comments through multiple Facebook
Pages, as mentioned above. The utilized web scraping literally in the form of automatic tools as it is much
easier and accessible rather than the manual process, which is much more complexities in its procedural
occurred. In the automatic tools used, the tool would load the inputted URLs and render the entire aimed
page. The tool would generate the desired result by inserting the three URLs of the dataset in earlier

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mentioned in an efficient and quick feasible format without any codes involved within it. As the comments
extracted, moreover, converted into a comma separated value (CSV) file formatted. Figure 1 in this case, by
the website's features, the three variants of posts comments are implemented as a single source of the data.
For the element of authenticity itself, the utilized datasets based on the three URLs source above, there is
less, or no data changed during the data scrape process, as it would keep the data integrity over time.
Figure 1. Sample of combined of three Facebook post's comments data
Each of the sample data output generates by the program, which for further processing to load the
data form the given file, or just read from the cache already pre-processed file. Also, there is a distinction
between testing processing and training data. They are being eliminated as the (CSV) file formatted was full
of empty entries with the support of the practice of functioned properties such as data model, wordlist, other
similar properties functioned to import the outputted raw data of variant of datasets that represented in the
percentage of negative comments, neutral comments, or positive comments, represented in Figure 2.
From the first post, it purposely uses the posts filled with the most negative comments, positive
comments, and mixed comments that have been showing sequential visualize. The graphical in Figure 3 is
exported by the implementation of Plotly's Python graphing library, which is called by using the renderers
framework) to generate the graphic chart of the raw data. In order to collect the visualization graphical, it
implied by using Python codes implemented in Jupyter-notebook IDE.
Figure 2. A combined sample of three Facebook post's comments data tuples visualizations
3.2. Data pre-processing segment
The sample Facebook Post's Comment raw data firstly pre-processed by utilizing the PIP libraries
include Pandas, NumPy, Plotly, and Scikit-learn library along with the implementation of other additional

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libraries. Pandas correctly implemented for open-source data analysis and manipulation tools such as for
opened a .csv extension file as in the case and for data framing and labeling of the sample raw data. NumPy
is suitable as cultivating for mathematical functions, which later used. Plotly best deal in mainly for graphical
visualization based on the existing data. Scikit-learn marvelous with learning or validation process occurred
in the analyzing process. These libraries are mandatory for the experiment as it loads many critical functions
to generate the data as aimed. Following the raw data are done imported; if the raw data want to be present to
the public, it requires a complete procedure to display the raw data entirely clear.
3.2.1. Dataset cleansing
The objective of dataset cleansing segment is to extract a value of the raw data strings contained
from the Facebook posts which use iteration method to identify, search, and separated each word occurred in
the selective sentence on the data of tuple-n-also being cleansed from any variants of unique characters,
escaped characters, and the rest itself. As with the help of utility of Regex library functions, e.g., escape
checked and compiled functions, following the iteration, e.g., using lambda function() which take an
argument of r: regex to executed its function with the user-defined based expression (",", ":", "", "=", and the
rest itself) to make a list of a word as desired, following the data saved and generated in an interprets of a
separated array of words. If it a success, then it would throw an output consist of a cleaned, separated array of
words data for approximately 600 arrays of words. As the algorithms are executed, it generates the following
algorithm's product, as they represent in Figure 4.
Figure 3. Sample of the graphic chart of combined of three Facebook post's comments data tuples
visualizations
Figure 4. Sample of the Facebook post's comments data following the cleansing process
3.2.2. Data tokenizing and stemming
The objective of data tokenizing or known as lexing (lexical analysis) and stemming segment, is to
convert a sequence of characters that occurred into a form of a sequence of tokens (strings with an assigned
and thus identified meaning as wikipedia.org source said). These processes make a partitioned of text data as
a sequence of words. Which consists of an identifier (x, y, z), a keyword (case, break, return), a separator

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([, {, ;), an operator (+, -, =), a literal (1.00e64, "data", true) or a comment (line, block); each word in the
reduced text data classify into one or more stems. The word that identifies as a "word" is checked in this
process. The text processing would occur in these steps and involve one of the most common libraries that
exist in Python, which is the natural language tool kit (NLTK). It has a function named NLTK.word_tokenize
function. The function work as the word_tokenize module is imported from the NLTK library; following
with the argument of "text" initialized with each row of data text inside it, within the row() function itself; the
row() function is passed in word_tokenize module and represents it outputs. Each row of data reduces into a
minimalized the redundancy of word, then are joined back as a list of one or more stems because the text of
data is in the form of English, the stemming method done using the Potters Stemmer algorithm in the process.
The result represents in Figure 5, with the four-column variables to distinguished the processed data.
Figure 5. Sample of the Facebook post's comments data following the tokenizing and stemming process
3.2.3. Construct the wordlist
Following the candidate data after the tokenized and stemmed procedure, as they represented in
Figure 6 occurred, the candidate data supposed to count as it would produce the information about the most
common occurrence of the words concatenated in the testing sample sentence. The candidate data supposed
to count as it would produce the information with more restrict qualification with typical English stopwords
(additional requirement added). However as the previously objective is to determine whether the dataset is as
interpret of the part of sentiment analysis (positive, neutral or negative) cluster, a word such as "not" and "n't"
comes big deal as it can influence the resulting process of the experiment of sentiment analysis significantly.
Hence it big deals with these, then the words ("not" and "n't") would be whitelisted in the whitelist variable.
Figure 6. Sample of the Facebook post's comments data following the calculation of word occurrences
process
Following the candidate data after being listed of all the occurrence of words and stored all the
candidate data, as they represent in Figure 7, visualized all the distinct occurrence of all words implied on a

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Facebook post comments data. The graph is generated by the utilization of the Plotly library of the Python,
which has a great deal with graphic analytical as the interpretation of the selective candidate data. The word
roughly ordered even though already shown the occurrence of each of the words. It needs to be simplified to
know that there is a connection between word occurrence and the sentiment. Each list of the individual word
in the previous graph is stored, also connected within the base [emotion], which results in several occurrences
words existed, with the [thanks] word variable is the most occurrence word (up to more than 70 repetitions).
Figure 7. Sample of the graphic chart of the occurrence of the Facebook post's comments data visualization
4. RESULTS AND DISCUSSION
This study method involved Facebook's posts, and comments are evaluated with a held-out test set,
of which 600 posts and comments samples data. This posts and comments uploaded in a range time of early
2020; these data evaluate near the date these posts posted to make it easier to see Facebook services
improvement that is reflected from these posts and comments. The scenarios are divide into three phases, and
each of the phases represents the post posted at a different time. The Figure 8 represents the post on January
30, 2020; Figure 9 represents the post on February 26, 2020; Figure 10 represents the post on March 25,
2020. It showed in Figure 10 that this post acquires more words as a negative word; these statistics tell that
the quality of the services on January 30, 2020, more said to have decreased. Also, in Figure 9, each word
that has many types of sentiment representation are summarized, and the quality of the services on February
26, 2020, yet still reaping negative replies-in Figure 10, already shown that on March 25, 2020, the most
collected word is the word that represents the positive sentiment. It signed that the data distribution has a
random pattern. So, from that three-phase can be summarized, the quality of the services on the date
mentioned is increasing and reaping positive replies. The implications of this research will undoubtedly be
useful for the development of the Facebook app in terms of the quality of service provided to Facebook users.
Figure 8. Sample of sentiment analysis of the Facebook post's comments data

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Indirectly, the assessment of services that utilize the concept of analysis based on sentimental users
has quite a complexity in the analysis that is not very complicated, with the support of python programming
base that implements natural language processing algorithm and functions in machine learning of NLTK, and
Scikit-learn library. There are two shortcomings of the implemented algorithm, resulting from the data
distribution process in this study, which is the skewed data such as the variables [post] and [account] in
Figure 9. It also happens similarly to positive and neutral comments; this shortcoming would lead to the
distinguish problematic between the positive, neutral, nor negative comments from one another. Followed by
the incomplete analytical process, which has not included in the data processing method, is the determination
of data precision. Without the precision method involved, it also would lead to the data distrust and
inconsistent problematic for the user who analyzed. Nevertheless, the strong point of these is less complexity
time, more comfortable to be coded and implemented, also generates a simple but straightforward graphics
rather than other algorithms such as Naïve Bayes for the data classification method or GloVe-trained model
with word2vec format.
5. CONCLUSION
As this research paper is completed, it reveals the implication that occurs as it is presented in the
authors' current research. The result of this paper shows an improvement advisor for Facebook's services
quality future from a glimpse of people's sentiment and perception of Facebook in recent times. This current
study would predict the improvement advice of Facebook's services development in the future. The future
developments of this study may utilize a large testing dataset, which would allow the test to predict more
accurately as improvement advice for Facebook's services development. Furthermore, the research will be
conducted in terms of precision of data processing accuracy, which includes three main factors in
determining the level of sentimental accuracy of the user, namely accuracy, Recall, and F1 Score factor. By
focusing on these factors, it will result in a fair, correct, and accurate analytic process.

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ACKNOWLEDGEMENTS
Thanks to the assistance from Bina Nusantara University as the authors' educational guidance and
supports all the researches involved in this paper.
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BIOGRAPHIES OF AUTHORS
Daniel Demetrius Albesta is a motivated person to do study in the database field, especially in
the multiple subjects related to the Database Administration and System Analysis. Take
educational courses on the Bina Nusantara University, and he got guidance on his practice,
which includes the Big Data comprehensive within it. Not only that, but Daniel also has a skill in
the UI/ UX Design of an application he made on the Bina Nusantara Festival (Bifest) annual
event. Currently, he works on the new research paper, which specifically about Big Data and
sentiment analysis flow within the IMDb contents. He is presently finishing his Bachelor of
Science in Computer Science, and from this point forward, the ambition he has is to be a data
engineer in an information technology (IT) department.
Michael Liong Jonathan, study at Bina Nusantara University for almost two years. In these past
years, he dedicated himself to learn database and how it works within the computer science
world. Until now, he is also working as a full-time junior programmer at Bina Nusantara
University always to keep up and learning more about database when on the other side trying to
find more experiences of having a career. While pursuing his career, currently, he is completing
and aiming for his Bachelor of Computer Science.
Muhammad Jawad is a Bina Nusantara University learner who is currently undergoing his
bachelor's degree in computer science. The interest he has is in the field related to the Internet of
Things (IoT) and other fields such as social media and digital marketing.
Oktovianus Hardiawan is a Bina Nusantara University learner-based at Jakarta, Indonesia, and
at the age of eleven, he was genuinely keen on the coding things. Introduced to the system of the
computer has, by his uncle, make him got interested in that. He will still recall the feeling that he
had to learn how machines applied within the computer, why it is needed, and what the
capability it has. That makes him interested in Computer Science and Mathematics. After
graduated from high school, he took a computer science major. At Bina Nusantara University, he
learned to Analysis of Algorithms, Data Structures, Programming Language, and Mathematics.
He has interested in Database systems related to System Analysis, and Programming focused on
mobile programming.
Derwin Suhartono is a faculty member of Bina Nusantara University, Indonesia. He got his
Ph.D. degree in computer science from Universitas Indonesia in 2018. His research fields are
natural language processing. Recently, he is continually researching argumentation mining and
personality recognition. He actively involves in the Indonesia Association of Computational
Linguistics (INACL), a national scientific association in Indonesia. He has his professional
memberships in ACM, INSTICC, and IACT. He also takes the role of reviewer in several
international conferences and journals.

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