Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries

Sunitha, D.; Patra, Raj Kumar; Babu, Nirmal Varghese; Suresh, A.; Gupta, S. C.

doi:10.1016/j.patrec.2022.04.027

Cited by 58 publications

(27 citation statements)

References 37 publications

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“…This shows that the input sample data sets have a high degree of similarity because the values are so close together. For the KNN classifier to work optimally, the number of neighbors in the prediction must be selected carefully [21]. The best KNN model can be found by experimenting with neighbor parameters ranging from 1 to 30 when using the elbow method.…”

Section: A Classification Methodsmentioning

confidence: 99%

An Investigation into Indonesian Students' Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis

Sarmini

Alhabeeb²,

Abusharhah³

et al. 2022

JOIV : Int. J. Inform. Visualization

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An anti-Covid-19 plan with social restrictions forced all Indonesian educational institutions to implement online learning in 2020. Strategy in early 2022, a new policy brought back online learning methods. Because of the rapid change and short adaptation period, online learning, which had been accepted as a solution for approximately two years, has become controversial. There were a variety of reactions in society, particularly on social media, after the rapid shift from face-to-face learning to online learning. This study will quantify text sentiment expressed on social media through machine learning. This study used SVM, RF, DT, LR, and k-nearest neighbors to develop a sentiment analysis model for use in sentiment research (KNN). The SVM- and RF-based sentiment analysis models outperform the others in cross-validation tests using data from the same Twitter social media site. Furthermore, RF can classify public opinion into three groups: positive, negative, and neutral, with a low error rate. The f1 values of our KNN-based model were measured at 75%, 65%, and 87% for negative, neutral, and positive tweets, respectively, which are slightly more accurate than previous studies with the same method and purpose.

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Section: A Classification Methodsmentioning

confidence: 99%

An Investigation into Indonesian Students' Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis

Sarmini

Alhabeeb²,

Abusharhah³

et al. 2022

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

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“…Sunitha et al. proposed a sentiment analysis technique to assess coronavirus‐related tweets [ 22 ]. Term Frequency‐Inverse Document Frequency, GloVe, Word2Vec, and fastText embeddings were used for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, they applied the Bidirectional Encoder Representations from Transformers model to compare the findings of LSTM and Bi-directional long short-term memory (Bi-LSTM) and then adopted the best model for sentiment analysis during the pandemic in India. Sunitha et al proposed a sentiment analysis technique to assess coronavirus-related tweets [22]. Term Frequency-Inverse Document Frequency, GloVe, Word2Vec, and fastText embeddings were used for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India

Dutta¹,

Das²,

Majumder

et al. 2022

CAAI Trans on Intel Tech

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The COVID‐19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID‐19‐related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India‐specific COVID‐19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short‐term memory, Bi‐directional long short‐term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.

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“…Some twitter articles report on the overall model tunings and findings on large machine learning models. These have showcased the power of big data analytics [57][58][59][60][61]. Still other articles report on the health findings about masking, vaccines, and overall health discourse [62][63][64].…”

Section: Qualitative Analysismentioning

confidence: 99%

What do people write about COVID-19 and teaching, publicly? Insulators and threats to newly habituated and institutionalized practices for instruction

Martinez

2022

PLoS ONE

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Covid represents major changes in teaching across the world. This study examined some of those changes through tweets that contained threats and insulators to habitualization of newer teaching practices. The investigator harvested tweets to determine sentiment differences between teaching and schools and teaching and online. Topic modeling explored the topics in two separate corpora. Omnibus Yuen’s robust bootstrapped t-tests tested for sentiment differences between the two corpora based on emotions such as fear, anger, disgust, etc. Qualitative responses voiced ideas of insulation and threats to teaching modalities institutionalized during the pandemic. The investigator found that ‘teaching and school’ was associated with higher anger, distrust, and negative emotions than ‘teaching and online’ corpus sets. Qualitative responses indicated support for online instruction, albeit complicated by topic modeling concerns with the modality. Some twitter responses criticized government actions as restrictive. The investigator concluded that insulation and threats towards habitualization and institutionalization of newer teaching modalities during covid are rich and sometimes at odds with each other, showing tension at times.

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Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries

Cited by 58 publications

References 37 publications

An Investigation into Indonesian Students' Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis

An Investigation into Indonesian Students' Opinions on Educational Reforms through the Use of Machine Learning and Sentiment Analysis

Aspect based sentiment analysis using multi‐criteria decision‐making and deep learning under COVID‐19 pandemic in India

What do people write about COVID-19 and teaching, publicly? Insulators and threats to newly habituated and institutionalized practices for instruction

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