Machine Learning based Sentiment Analysis of Coronavirus Disease Related Twitter Data

Krishnan, Hema; Pankajkumar, G; Poosari, Aiswarya; Jayaraj, Anagha; Thomas, Christy; Joy, Grace Mol

doi:10.1109/icsccc51823.2021.9478145

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…Deep neural networks, on the other hand, break issues down into layers and are regarded as a great tool for extracting valuable clues for more accurate future predictions [21]. e sentiment analysis [22] was also utilized to measure recall, precision, and accuracy. e accuracy and precision of the findings are 0.86 and 0.827, respectively.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

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In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

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“…Many sentiment analysis studies [4][5][6][7] use tweets as training data for machine learning. Some studies [8][9][10] focus on using sentiment analysis associated with tweets to assess the impact of coronavirus.…”

Section: Related Researchmentioning

confidence: 99%

“…Linebot: evaluate your problem as a job category and result of sentiment analysis is negative (8). Linebot: recommends articles that belong to the field of job to users (9).…”

Section: System Demonstrationmentioning

confidence: 99%

Sentiment-based Chatbot using Machine Learning for Recommendation System

Hsu

Liao

2022

Preprint

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Emotions are part of human mental activities and play an extremely important role in the decision-making process of daily life. Publishing posts in natural language through social websites is part of people’s lives. These posts can reflect the emotion state of users. It is important to study how to use machine learning technology to interpret the sentiment analysis of posts. The research proposed a general framework based on sentiment analysis and machine learning, called Sentiment Analysis and Machine Learning Recommendation Framework (SAMLRF), including data preparation module, sentiment analysis module, recommendation module, human machine module and cloud computing module for a chatbot to facilitate user interaction to make recommendations. To verify the modular function of the proposed SAMLRF, this research develops a Sentiment-based Article Recommendation Linebot (SARL), which provides an API interface for chatbots to activate the system through a webhook mechanism. The performance and accuracy of four machine learning and two deep learning algorithms were compared, including the decision tree, logistic regression, support vector machine and gradient boosting decision tree, simple recurrent neural networks and long short-term memory, operating in Spark cloud computing environments. Experiments show that the decision tree algorithm for sentiment analysis is relatively better in computing performance and test accuracy.

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“…Nair et al 2021) for mining public opinion from COVID-19 tweets to help out public health organizations, as well as government officials [12]. In [4], [13]- [17], the authors had used Multinomial NB, Decision Tree (DT), Logistic Regression, Support Vector Machine (SVM), Random Forest Classifier with different features like weighted TF-IDF and n-gram to investigate sentiment analysis from the tweets related to COVID-19 pandemic to mine public anxiety.…”

Section: Related Workmentioning

confidence: 99%

“…Researchers have been working hard to determine public sentiment in a pandemic situation. Sentiment analysis is a part of Natural Language Processing (NLP) which can be identified using various machine learning and deep learning approaches [4], [5]. In the context of computing, it can also be considered as a part of the broad area of data science with textual analytics [2].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

HOSSAIN¹,

Assaduzzaman²,

Mynoddin³

et al. 2022

Preprint

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Sentiment analysis is a process of extracting opinions into the positive, negative, or neutral categories from a pool of text using Natural Language Processing (NLP). In the recent era, our society is swiftly moving towards virtual platforms by joining virtual communities. Social media such as Facebook, Twitter, WhatsApp, etc are playing a very vital role in developing virtual communities. A pandemic situation like COVID-19 accelerated people's involvement in social sites to express their concerns or views regarding crucial issues. Mining public sentiment from these social sites especially from Twitter will help various organizations to understand the people's thoughts about the COVID-19 pandemic and to take necessary steps as well. To analyze the public sentiment from COVID-19 tweets is the main objective of our study. We proposed a deep learning architecture based on Bidirectional Gated Recurrent Unit (BiGRU) to accomplish our objective. We developed two different corpora from unlabelled and labeled COVID-19 tweets and use the unlabelled corpus to build an improved labeled corpus. Our proposed architecture draws a better accuracy of 87% on the improved labeled corpus for mining public sentiment from COVID-19 tweets.

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Machine Learning based Sentiment Analysis of Coronavirus Disease Related Twitter Data

Cited by 9 publications

References 8 publications

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Sentiment-based Chatbot using Machine Learning for Recommendation System

A Deep Learning Approach for Public Sentiment Analysis in COVID-19 Pandemic

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