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Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media
Open accessAbstract
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper analyzes COVID-19 tweets and argues that highly retweeted/popular tweets skew negative or neutral while unique tweets are more positive or neutral, with word clouds and word frequencies showing little actionable guidance. It reports up to 81% accuracy for a Logistic Regression/Tfidf trigram model on DATA_SET 1 and 79% F1 for a Gaussian fuzzy rule-based sentiment model.
Key findings
- In DATA_SET 1 (226,673 tweets), TextBlob labeled 98,995 positive, 53,414 negative, and 74,264 neutral tweets; AFINN labeled 66,481 positive, 101,607 negative, and 58,585 neutral tweets. In the most-retweeted DATA_SET 2 (22,985 tweets), TextBlob labeled 6,728 positive, 3,597 negative, and 12,660 neutral tweets; AFINN labeled 4,604 positive, 9,273 negative, and 9,108 neutral tweets.
- Best classifier accuracy was 81% for DATA_SET 1 using Logistic Regression with trigrams under the Tfidf vectorizer, and 75% for DATA_SET 2 using Logistic Regression with bigrams under the Tfidf vectorizer.
- The proposed Gaussian membership function fuzzy rule-based system achieved F1-score 79%, precision 82.13%, recall 84.56%, sensitivity 91%, and mean absolute error 0.16, compared with the triangular membership system’s F1-score 78%, precision 80.45%, recall 82.61%, sensitivity 93%, and mean absolute error 0.19.
- A non-parametric independent t-test on positive and negative sentiment values yielded t-value = 2.578 and p-value = 0.035, which the authors use to support their claim that social media was not useful enough during the COVID-19 outbreak.
Methods
- The study used two Twitter datasets: DATA_SET 1 with 226,668 unique tweets from December 2019 to May 2020 collected via keywords such as “corona,” “covid,” “sarscov2,” “covid19,” and “coronavirus”; DATA_SET 2 with approximately 23,000 tweets having at least 1,000 retweets from 1 January 2020 to 23 March 2020.
- Tweets were cleaned, labeled using SentiWordNet, TextBlob, and Afinn, then transformed with Count Vectorizer and Tfidf Vectorizer using unigrams, bigrams, and trigrams; classifiers included MultinomialNB, BernoulliNB, LinearSVC, AdaBoost, RandomForest, and LogisticRegression, with Doc2Vec models DBOW, DMC, and DMM also tested.
- A Mamdani-style fuzzy rule-based model was proposed using Gaussian membership functions for positive score, negative score, and sentiment output, with seven inference rules and centroid defuzzification; VADER was used to label data for this fuzzy model.
Limitations
- The paper states that the work “does not have the features to attend multilingual tweets,” which is listed as future work.
- The paper states that further improvement may be possible by incorporating more membership grades in the fuzzy model.
- Evident from the text: the analysis is limited to Twitter data over specific pandemic periods, and the datasets have unequal numbers of positive, negative, and neutral tweets, which the authors address by also using a Random Forest classifier to create balance.
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Claims the paper makes
22 Own result · 13 No basis given · 9 Own interpretation · 7 Not checked yet · 2 Own method
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The implemented model gives an accuracy of above 75% on both the datasets used for this purpose.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Introduction
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The accuracy for the said model yields up to a permissible rate of 79%.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The research demonstrates that though people have tweeted mostly positive regarding COVID-19”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Abstract
boosted- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“yet netizens were busy engrossed in re-tweeting the negative tweets”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but to our utter shock, we found out that the number of negative or neutral tweets is remarkably more in the re-tweeted DATA_SET 2 by both the processes that have been used for the same.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > State-of-art classifier based proposed model
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Abstract
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“and that no useful words could be found in WordCloud or computations using word frequency in tweets.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Abstract
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Now, as the p-value is less than the considered threshold of 0.05, then we can claim that there is noteworthy dissimilarity between the two means of positive as well as negative sentiment values.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
plain- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 22
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“but the tweets those are negative or neutral in nature are being re-shared most.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Background > Motivation and justification of the proposed work
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The unique dataset has hence been analyzed and presented to support the claim that though people uniquely are spreading positive vibes pertaining to the pandemic”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Background > Motivation and justification of the proposed work
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As a contribution to the society, this paper has demonstrated with facts that how it is shocking that though people are initially sharing positive and neutral data but further users are re-tweeting those tweets which are negative in nature.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Introduction
boosteddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For DATA_SET 1, there is a maximum number of positive and negative tweets”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > State-of-art classifier based proposed model
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“analyzing the posts, it has been seen that most of them have misled people by posting false data and figures.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, the Mean Absolute Error value also stands as an indicator of the efficiency of the system, although the Log Loss measure is lower for the Triangular counterpart.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > Proposed fuzzy rule based model
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It has been seen that the most retweeted users in Twitter® are @realDonalTrump, @WHO etc.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Background > Motivation and justification of the proposed work
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While the unique tweets result in positive”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > State-of-art classifier based proposed model
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“These too display words that do not prove any efficiency in representing a viable solution during emergencies.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > State-of-art classifier based proposed model
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Rests on an assumption: The WordCloud visualization adequately represents the content and discourse of the tweets.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Obviously, the time taken for the model to train DATA_SET 1 is much more compared to the time taken for DATA_SET 2 due to its larger size.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Tweets from WHO were also analyzed but they also failed to provide precise information which could be retrieved to better deal with the disease.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Discussions
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In the paper
“DATA_SET 1 is ten times the size of DATA_SET 2 and yet the model exhibits more or less the same behavior for both the datasets.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, the aforesaid model is estimated on the testing data and the accuracy yields up to 81% and 75% accuracy for DATA_SET 1 and DATA_SET 2, respectively.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
hedged- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“| No. of positive tweets | 98 995 | 66 481 | 6728 | 4604 |”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > State-of-art classifier based proposed model
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
- Rests on an assumption: The sentiment labeling methods (TEXTBLOB, AFINN, VADER) correctly capture the sentiment of the tweets.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The work done in the paper also relates to the title as how instead of the magnanimous popularity of social media, it is leading to the dwindling of tweets and for which the same cannot be considered as a trusted source.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Introduction
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The figures clearly depict that people showed confused behavior in social media during the pandemic outbreak and portrayed emotions likewise.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > State-of-art classifier based proposed model
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, the proposed system exhibits less sensitivity towards change of inputs, thereby indicating the stability of the system.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > Proposed fuzzy rule based model
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Twitter® is one of the most trendy micro blogging sites, which is considered as a crucial depository of sentiment analysis [4].”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Background > Motivation and justification of the proposed work
What the sources say
- Not checked yetcites [4] FÅ. Nielsen, A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903. 15 Mar.
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In the paper
“It is generally observed that TextBlob produces normalized scales in contrast to Afinn [38].”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > State-of-art classifier based proposed model
What the sources say
- Not checked yetcites [38] E. Kouloumpis, T. Wilson, J. Moore, Twitter sentiment analysis: The good the bad and the omg!, in: Fifth International AAAI conference on weblogs and social media. (2011, July).
hedged- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“hence emoting uncertainty over the state of the disease.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > State-of-art classifier based proposed model
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This work does not have the features to attend multilingual tweets, which could be considered as a probable future work in this direction.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Conclusion
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In the paper
“Here, K-fold Cross Validation [57] is also used as a resampling technique to check the steadiness of the model.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
What the sources say
- Not checked yetcites [57] Mikolov, 2013, Distributed representations of words and phrases and their compositionality, Adv. Neural Inform. Process. Syst.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In general, LinearSVC classifiers prove efficient for text data classification cases.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Gaussian membership function (MF) [41] depends on two of its parameters, the mean of the data and its standard deviation.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Proposed methodologies > Proposed fuzzy rule based model
What the sources say
- Not checked yetcites [41] Hameed, 2011, Using Gaussian membership functions for improving the reliability and robustness of students’ evaluation systems, Expert Syst. Appl. source
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It has been observed that for self-created analyser models, there is no perfect classification algorithm that exists.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The Naïve Bayes classifier models are very effective in making predictions for sentiment analysis and are based on Bayes’ Theorem [55].”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
What the sources say
- Not checked yetcites [55] https://en.wikipedia.org/wiki/Naive_Bayes_classifier, Anonymous, retrieved 12th September, 2020.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
Misleading information on social media causes depression, stress, anxiety and directly affects mental health.
“it is now becoming a source of depression, stress and anxiety because of misleading information posted on social media.”
“The mental health is directly affected because of the rapid spread of false information on social media.”
The paper relies on this without support.
People's tweets and sentiments reflect the actual mental/emotional state of social media users.
“We have implemented various state-of-the-art deep learning classifiers to extract the actual sentiment of social media users during this pandemic.”
“Sentiment analysis is the gathering of people’s views regarding any event happening in real life.”
The paper relies on this without support.
Accurate and not popular messages are being seen, shared or followed at par with the outbreak of Corona Virus in the world.
“complete unique tweets and the tweets that have at least 1,000 re-tweets based on the logic that accurate and not popular messages are being seen, shared or followed at par with outbreak of Corona Virus in the world, have been considered in this paper.”
The paper relies on this without support.
The mental and physical health of the global population is directly proportional to the pandemic disease.
“The mental and physical health of the global population is found to be directly proportional to this pandemic disease.”
The paper relies on this without support.
The tweet datasets (DATA_SET 1 and DATA_SET 2) are representative of public sentiment worldwide during the COVID-19 pandemic.
“One of the major outcomes of this paper is the establishment of the fact from DATA_SET 1 that people worldwide has shown positive sentiments towards the disease.”
The paper relies on this without support.
The sentiment labeling methods (TEXTBLOB, AFINN, VADER) correctly capture the sentiment of the tweets.
“Step 2: VADER sentiment lexicon is used to label the data into three classes, viz., positive, negative and neutral based on their polarity scores.”
The paper relies on this without support.
The WordCloud visualization adequately represents the content and discourse of the tweets.
“The majority of the words that have been portrayed in each of the sentiments has been visualized using the WordCloud modules.”
The paper relies on this without support.
The number of retweets reflects the popularity or importance of a tweet.
“But, as mentioned earlier, the tweets with minimum 1000 retweets were considered for this experiment.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“I employ the AFINN lexicon developed by Finn Årup Nielsen (Nielsen, 2011) one of the most used lexicons for sentiment analysis (Akhtar et al., 2020;Chakraborty et al., 2020;Hee et al., 2018;Ragini et al., 2018)”
From The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions (2022) · cites this paper as Chakraborty et al., 2020
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Abstract
“around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Background > Motivation and justification of the proposed work
“For the second case, data from Twitter® have been collected for this work between the span of 1st January, 2020 to 23rd March, 2020.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
“It is observed that the Logistic Regression classifier working on the DBOW+DMC model gives the best result as far as time taken for execution is concerned.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
“| 9 | DBOW+DMC | BernoulliNB() | 0.526 | 0.16 | | 10 | DBOW+DMC | LinearSVC() | 0.622 | 36.35 | | 11 | DBOW+DMC | AdaBoostClassifier() | 0.548 | 29.34 | | 12 | DBOW+DMC | LogisticRegression() | 0.628 | 14.12 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Background > Motivation and justification of the proposed work
“A total of 2, 26, 688 tweets have been collected from IEEE Dataport [5].”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Background > Motivation and justification of the proposed work
“The first dataset contains distinctive 2, 26, 668 tweets crawled through ‘corona’, ‘covid’, ‘sarscov2’, ‘covid19’ and ‘coronavirus’ keywords.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
“Finally to fit the model, the data have been categorized into train, validate and test sets.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
“90% data from training set, 5% from validation set and rest 5% from the training set have been used.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
“It is observed that Logistic Regression classifier performs best in all the test cases.”
§ Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media > Experimental results
“| DATA_SET 1 | DBOW | LinearSVC() | 0.57 | | DATA_SET 1 | DMC | BernoulliNB() | 0.47 | | DATA_SET 1 | DMM | LinearSVC() | 0.56 | | DATA_SET 1 | DBOW+DMM | LinearSVC() | 0.60 |”
Citations
Cites
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
An experiment in linguistic synthesis with a fuzzy logic controller
Mamdani, E. H., & ASSILIAN, S. (1975) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(75)80002-2 · Cited by 5,795
An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller
Mamdani, E. H., & ASSILIAN, S. (1999) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1973.0303 · Cited by 5,627
Using social and behavioural science to support COVID-19 pandemic response
Bavel, J. J. V., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., Crockett, M. J., Crum, A. J., Douglas, K. M., Druckman, J. N., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0884-z · Cited by 4,964 · cited 1×
“Different strategies and measures are taken by public to control the spread of misinfor-mation [26] which has potentially dangerous outcomes.”
Hui, D. S., I Azhar, E., Madani, T. A., Ntoumi, F., Kock, R., Dar, O., Ippolito, G., Mchugh, T. D., Memish, Z. A., Drosten, C., et al. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.01.009 · Cited by 3,809
Wilder-Smith, A., & Freedman, D. O. (2020) · Journal of Travel Medicine · doi:10.1093/jtm/taaa020 · Cited by 2,727 · cited 1×
“A non-pharmaceutical intervention reduces human contact within the population [24] and therefore constrains the spread of COVID-19.”
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
Li, S., Wang, Y., Xue, J., Zhao, N., & Zhu, T. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17062032 · Cited by 1,835
Systematic Literature Review on the Spread of Health-related Misinformation on Social Media
Wang, Y., McKee, M., Torbica, A., & Stuckler, D. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.112552 · Cited by 1,806 · cited 1×
“For example, online rumors accusing 5G deployments for causing COVID-19 led to mobile phone masts being attacked in the UK [28].”
Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G. (2020) · Psychological Science · doi:10.1177/0956797620939054 · Cited by 1,715
Research works printed but not linked: 5
- Li, 2020, The impact of COVID-19 epidemic declaration on psychological consequences: a study on active weibo users, Int. J. Environ. Res. Publ. Health, 17, 2032, 10.3390/ijerph17062032 · DOI · Paper page
- E. Kouloumpis, T. Wilson, J. Moore, Twitter sentiment analysis: The good the bad and the omg!, in: Fifth International AAAI conference on weblogs and social media. (2011, July).
- Q. Le, T. Mikolov, (2014, January). Distributed representations of sentences and documents, in: International Conference on Machine Learning, pp. 1188-1196.
- I. Rish, An empirical study of the naive Bayes classifier, in: IJCAI 2001 workshop on empirical methods in artificial intelligence, Vol. 3(22) (2001, August), pp. 41-46.
- Olsen, 2004, Cross-sectional study design and data analysis, College Entrance Exam. Board, 26, 2006
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Self-citationKodati, D., & Dasari, C. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107361 · Cited by 20
Ismail, L., Shahin, N., Materwala, H., Hennebelle, A., & Frermann, L. (2023) · IEEE Access · doi:10.1109/access.2023.3343121 · Cited by 19
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Metadata
- Authors
- Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E.
- Affiliations
- King Faisal University (SA)
- Christ University (IN)
- Cairo University (EG)
- Corresponding author
- Bhattacharyya, Siddhartha
- ORCID
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 97
- Pages
- 106754
- ISSN
- 1568-4946 (ISSN-L)
- PMC
- PMC7521435
- OpenAlex
- W3088352144
- Semantic Scholar
- 13aa8ef82c8a0994da27f3edcf9a0eb90a6fea73
- Cited by
- 375
- References
- 53
- Citations by year
- 2026: 6 · 2025: 24 · 2024: 55 · 2023: 80 · 2022: 115 · 2021: 94 · 2020: 1
- FWCI
- 28.50
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Applied Soft Computing (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace VŠB-TUO (VŠB-TUO) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- COVID-19 pandemic, sentiment analysis, Twitter, retweets, tweet popularity, deep learning classifier, Gaussian membership function, fuzzy rule base, public perception of pandemic, World Health Organization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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