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| Paper | |
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 edit
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| id | Vol-3194/paper20 |
| wikidataid | Q117345145→Q117345145 |
| title | A Survey of Sentimental Analysis Methods on COVID-19 Research |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper20.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/UmairM22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
A Survey of Sentimental Analysis Methods on COVID-19 Research
A Survey of Sentimental Analysis Methods on
COVID-19 Research
Areeba Umair1 , Elio Masciari1,2
1
Department of Electrical Engineering and Information Technologies, University of Naples, Federico II
2
Institute for High Performance Computing and Networking (ICAR), National Research Council, Naples, Italy
Abstract
In this era of social media, people share anything they feel or experience on social media in the form of
posts or comments. These posts, comments or reviews of the people can be analyzed using sentimental
analysis, which is emerging field in text mining. COVID-19 has people’s life all over the globe and thus
has declared as pandemic. Due to COVID, people are feeling panic, anxiety, rage, sorrow, misery, stress
and other issues. In this review, we have presented the sentimental analysis data sources, approaches,
scenarios, methods and tools by comparing thirty studies. The results illustrated that most researchers
have used SVM and Naive Bayes for sentimental analysis on COVID research. We also concluded that
most of the researchers work on the sentiments of students, reopening sentiments, vaccine sentiments
etc.
Keywords
Social media Big Data, Sentiments related to COVID, Social Media Reviews, Data analytic, COVID-19
1. Introduction
Now-a-days, many people use social networks to express their opinion, thoughts or feedback
about anything, [1]. In this era of technology, almost all the industries provide their customers
with the ability to buy product online and also share their reviews or feedback on their website of
social media pages [2]. This feedback can be positive or negative which can help other customer
in making decision and help the industry to improve the product according to the customer
need [3]. Such kind of review data on internet can be used in extraction of sentiments from the
raw data that can be used for well-being of the society as well as business or organization [4],
[5]. Sentimental analysis is the natural language processing tasks in which text is classified into
positive, negative or neutral sentiments based on their meanings in the sentence. There are three
types of sentimental analysis i.e. document level, sentence level and aspect level sentimental
analysis. In order to gain the fine grain sentimental expression, aspect level sentimental analysis
is used [1]. Let’s take an example to understand aspect level sentimental analysis. "The food
is very tasty but its quality is low". In this example, "very tasty and "low" show two different
sentiments i.e. positive and negative respectively. The traditional sentimental analysis methods
have been eliminated due to advancement in artificial intelligence [3].
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ areeba.umair@unina.it (A. Umair); elio.masciari@unina.it (E. Masciari)
https://www.docenti.unina.it/elio.masciari (E. Masciari)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
� The whole world is facing the biggest challenge in the form of COVID, which has destroyed
the economy of many under-developed countries [6]. Corona-virus was discovered in Wuhan,
China in the month of December 2019 and it has started spreading in the world and thus declared
as pandemic. According to John Hopkins University, 435, 427, 191 people have been affected
due to COVID, thus causing 5, 966, 417 number of deaths till 27 February 2022. People are
facing different psychological problems due to COVID such as anger, depression, fear, and many
others.
The traditional machine learning methods and deep learning methods are available to resolve
the sentimental classification problems [7], [8]. The traditional ML (machine learning) classifiers
for sentimental classification are Support Vector Machine (SVM) and Naive Bayes however,
deep learning methods for sentimental classification are Recurrent Neural Network (RNN) and
Convolutional Neural Network (CNN). These methods extract meaningful features automatically
[1]. RNN has a recurrent nature due to which it suffers from gradient vanishing problem and
CNN has short-comings for sequential dependencies. Thus, the literature shows the different
issues and limitations in the exiting approaches such as low accuracy and performance and
high complexity [3]. The inconsistent sentimental polarity in the sentence causes the word
dependency to be weaken. In such scenario, attention mechanism can be fruitful for sentimental
classification tasks.
In this research, we have collected thirty primary studies related to sentimental analysis with
respect to COVID-19 and performed the survey. The purpose of the survey was to identify the
main data sources which are providing COVID-19 related data and the widely used applications
that have been applied on such data. This survey also identifies the applications or topics on
which research is being processed with respect to COVID-19 sentimental analysis. At the end
the future implications of COVID-research have also been presented in this survey.
2. Methodology
Thirty primary studies were selected for the comparison and their review was performed. Table
1 has six columns, first columns shows the references while in second column, data sources used
in COVID-19 research have been mentioned. The purpose of mentioning the data sources or data
sets is to assist the new researchers in collecting the similar datasets for their research. We have
also mentioned the volume of the dataset used in the primary studies in column 3. The methods
and approaches frequently used for sentimental analysis of COVID-19 have been specified in
column 4. The column 5 illustrated the application scenarios for COVID sentimental analysis
research, it can give new directions for the future research. The future research directions have
been shown in column 6.
2.1. COVID-19 Data-sets
During the COVID pandemic, many people experience different mental issues which caused
their emotions to change. The people used social media to express these emotions. Therefore,
the social media provide huge amount of data to understand the peoples feelings and reactions
to the situations they faced during pandemic. The data sources for COVID-19 research have
been shown in Table 1. It illustrates that the main data source during COVID was twitter.
�Twenty four out of thirty primary studies used twitter as a source in their research. However,
the remaining data sources were WeChat account, Yelp, Reddit, and other Media forums.
Twitter: Twitter has been used worldwide for sharing the thoughts and opinions. It is most
popular app having 81.47 million users [9]. People post their feelings in the form of "tweets".
According to research in [10], almost 200 billion tweets are published in one year.
2.2. Sentiments classification methods
With the increase of social media platforms and social media data, more powerful analytical tools
should be developed. Different approaches were adopted for COVID-19 research to perform
the sentimental analysis. They can be divided into three different types i.e. machine learning,
lexicon based and hybrid.
2.2.1. ML and Deep Learning (DL) Methods:
The ML methods which can be used for sentimental analysis are supervised learning approaches
and unsupervised approaches.
The supervised learning works on the labelled data . Different researchers used different
sentimental analysis methods on COVID data as seen in Table 1. In and [11], Naive Bayes
algorithm was used as a supervised learning method for sentimental classification. Naive Bayes
uses the Bayesian theorem given in equation 1.
𝑃 (𝐻|𝑋) = 𝑃 (𝑋|𝐻)𝑃 (𝐻)/𝑃 (𝑋) (1)
Support vector machine works by finding the hyper-plane in the whole data by creating high
dimensional feature from he feature space. [12], [13] and [14] used SVM in their research for
sentimental analysis. Decision tree found different decision rules from the entire dataset and
used them to train its model. Random forest also chooses random features and instance from
the entire dataset. It has been used by [15], [13] and [14]. Many other researchers used other
sentimental analysis techniques such as KNN Linear Regression [16], Logistic Regression [11],
[14], LSTM [17], [13], RNN [18] and BERT model [10], [9] etc.
The unsupervised learning ML methods uses unlabeled data. There are different methods
that have been applied on sentimental analysis during COVID. The researchers used K-means
clustering in . However, many other studies such as [19], [20], [21], [14], [22], [23] have used
Latent Dirichlet Allocation (LDA).
2.3. Application scenarios on COVID-19 data
COVID has effected people’s life and thus they are facing different psychological issues. Many
researchers pursued their research to analysis the people’s sentiments during COVID-19.
2.3.1. Mental health analysis of students during the lockdown
To control the spread of COVID, social distancing was applied which reduced the human-to-
human interactions. Many countries imposed lockdown, and closed the airspace, educational
and other institutes. Due to lockdown, people specially students had to stay far away from their
�homes, stuck in their hostels, and had to quit their educational activities, which causes anxiety
and stress in students. Students express their sentiments using social platforms and researchers
tried to explore their sentiments [20], [24] and students [25]. In [18], [26], [13], [11], [27], [28],
[21], [22], [19], [29], [30], twitter data was used to understand the people’s sentiments during
lockdown.
2.3.2. Reopening after COVID-19:
Coronavirus has effected the lives of billion of peoples directly or indirectly. It has caused
economical crisis all over the world which is a hurdle towards reopening [31]. The long-term
closing of economy is a threat for any country to survive. Due to these reasons, people are
forcing to reopen the businesses and going to normal life [32]. Hence, in [32] and [31], the
researchers put their efforts on the discovering what are people thinking about re-opening after
COVID-19.
2.3.3. Restaurant reviews
In today’s digital era, the customers can share their opinion and feedback about quality of
product or services they use from different organizations. These reviews help other customers
to make decisions when they are about to use the service and product. The online reviews
are associated with the star rating which effect the revenue of the restaurant. During COVID,
special SOPs were announced for the restaurants and people were very concerned about the
COVID-spread. Therefore, many restaurants got negative reviews for cold outdoor area and
slow service. Researchers analyzed the people’ feedback about restaurants which helped the
management of restaurants to maintain a quality food and ambience [15].
2.3.4. Vaccine sentiments and racial sentiments
The development of COVID vaccine can be useful to control the spread of COVID. Therefore,
it many industries put their efforts and develop different kind of vaccines. But, to control the
COVID with vaccines, the acceptance and receiving of vaccines is the main requirement [33].
If people are not willing to get themselves, it will be a clear hurdle in the control of COVID
[34]. Researchers analyzed the public sentiments about vaccines in [9]. COVID also caused the
feelings of discrimination across the boarders and therefore people became more racists [12].
3. Comparison of Studies
Table 1 shows the summary of the comparison of the primary studies used in this survey.
Table 1: Comparison of methods and scenarios used for analyzing people’s sentiments related
to COVID-19
�Ref Data Volume of Methods Application Future Direction
Source/set Data
[15] Yelp 112,412 GBDT, RF, Analyze restaurant Restaurant locations.
LSTM, SWEM reviews
[28] Tweets 20,325,929 CrystalFeel Trends of fear, anger, sad-
Include other media
tweets ness, and joy platforms.
[35] Tweets 500,000 TextBlob Finding tweets polarity Explore other social
tweets media
[9] Tweets 57.5M BERT Vaccine sentiments Real-time social media
monitoring
[18] Tweets N/A NLP, RNN Analyze sentiments Visualization, cluster-
ing and classification
[12] Tweets 3,377,295 SVM Racial sentiment changes Temporal changes in
racial attitudes
[36] Tweets 840,000 TextBlob, LDA, identification of Anxiety, Perception changes for
stress, and trauma different biographies
[27] Tweets 57 454 NLP Analyse the characteris- N/A
tics of polish COVID-19
[26] Tweets 370 WordCloud Sentimental analysis N/A
[32] Tweets 293,597 Binary logit Reopening sentiment Socioeconomic and
model household information
[13] Tweets 7528 TextBlob, CNN- Perform sentiment analy- Use deep learning ap-
LSTM, RF, SVC, sis proaches
ETC, DT,
[11] Tweets 900000 NB, LR, Public sentiment associ- Include news articles
ated with the progress of and personal commu-
Coronavirus nications data.
[31] Tweets 293,597 N-gram, R pack- Reopen Sentiments Replicate on other so-
ages Syuzhet cial media data
and sentimentr
[29] Tweets 16 million TClustVID Investigate Topics and Explore other data
Sentiment repositories.
[21] RateMDs 55,612 PORs TF–IDF, LDA Patients views Trend in high death
and recovery rate
[22] Tweets 4 million LDA, NLP COVID-19–related senti- Explore public trust
ments
[30] Tweets 13 million Dynamic Topic Detecting Topic More specific topics
Models
[23] Qingbo N/A LDA Emotional change Precise location infor-
mation
�4. Conclusion
Twitter based sentimental classification is a new paradigm in the social media research. A review
of almost thirty primary studies was performed in our research. The comparison of data sources
used, volume of data used, approaches, and application scenarios with respect to COVID-19 was
established. This survey presents its contribution in the field of sentimental analysis and open
doors for the new researchers. This survey paper shows that twitter is the most popular data
source for sentiments analysis and Naive Bayes and SVM are the algorithms which researchers
used for sentimental analysis during COVID. During COVID-19, various researchers worked on
the different dimensions such as mental health of students, reopening sentiments, restaurants
reviews and vaccine sentiments. Thus, the use of advanced methods of machine learning and
deep learning along with the social media data can explore more interesting topics in future.
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