CTC: COVID-19 Tweet Classification using CT-BERT

CTC is my submitted work to the Information Retrieval from Microblogs during Disasters (IRMiDis) Track at the Forum for Information Retrieval Evaluation (FIRE) 2022. Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus experience a mild to moderate respiratory illness and recover without requiring special treatment. However, some become seriously ill and require medical attention. Vaccines against coronavirus and prompt reporting of symptoms saved many lives during the pandemic. The analysis of COVID-19-related tweets can provide valuable insights regarding the stance of people toward the new vaccine. It can also help the authorities to plan their strategies based on people's opinions about the vaccine and ensure the effectiveness of vaccination campaigns. Tweets describing symptoms can also aid in identifying high-alert zones and determining quarantine regulations. The IRMiDis track focuses on these COVID-19-related tweets that flooded Twitter. I developed an effective classifier for both Tasks 1 and 2. The evaluation score of my submitted run is reported in terms of accuracy and macro-F1 score. I achieved an accuracy of 0.770, a macro-F1 score of 0.773 in Task 1, and an accuracy of 0.820, a macro-F1 score of 0.746 in Task 2. I enjoyed the first rank among other submissions in both the tasks. © 2022 Copyright for this paper by its authors.

Analysing sentiment change detection of Covid-19 tweets.

The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.

Memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks

The rapid advancements of social media networks have created the problem of overloaded information. As a result, the service providers push multiple redundant contents and advertisements to the users without adequate analysis of the user interests. The content recommendation without user interests reduces the probability of users reading them and the wastage rate of network load increases. This problem can be alleviated by providing accurate content recommendations with consideration of users' precise interests and content similarity. Content centric networking has been developed as the trending framework to satisfy these requirements and improve access to relevant information and reception by the desired user. The uses of message entity by giving a proper name, the users' real-time interests are identified and then the accurate and popular contents with high contextual similarity are recommended. An efficient content recommendation scheme is presented in this paper using Memory Augmented Distributed Monte Carlo Tree Search (MAD-MCTS) algorithm for ensuring minimum energy consumption in the CCN. The big data context of the users' social media data is considered in this study so that the complexity can be visualized and controlled to minimize the network complexities. Experiments are conducted on a benchmark as well as an offline collected Twitter dataset on Covid-19 and the results implied that the accuracy and convergence of the proposed MAD-MCTS outperform the other content recommendation algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser

BackgroundInformation is essential for growth;without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of the certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons. AimsThis study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the coronavirus pandemic (COVID-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning.MethodologyThe hashtag ‘COVID-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a Twitter tokenizer, while TextBlob and Valence Aware Dictionary for Sentiment Reasoning (VADER) were used for text mining and sentiment analysis, respectively. Topic modelling was done with Latent Dirichlet Allocation and visualized with Multidimensional scaling.ResultsThe result of the VADER sentiment returned 39.8%, 31.3%, and 28.9%, positive, neutral, and negative sentiment, respectively, while the result of the TextBlob sentiment returned 46.0%, 36.7%, and 17.3%, neutral, positive, and negative sentiment, respectively.ConclusionWith all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of COVID-19. Also, know the opinion and challenges of people, then deal with the problem of misinformation. It is concluded that with popular belief a significant number of the populace regards COVID-19 as a virus that has come to stay, some believe it will eventually be conquered.

Sentiment Analysis of Covid-19 Tweets using Twitter Database-A Global Scenario

Sentiment analysis falls within the category of Natural Language Processing (NLP) technology. Behaviour analysis and recommendation employ sentiment analysis. On the dataset of COVID-19 tweets, random forest Classifier, Decision Tree, Support Vector Machine (SVM), and Random Forest are the machine learning methods under consideration for sentiment analysis. The total number of tweets used for this research is 179108. Pre-Processing is used to clean and analyze these tweets. The sentiment analysis of COVID-19 tweets used in this study reveals the individual experiences of those affected by the epidemic. The primary goal of this survey is to analyze people's experiences. It helps to better understand the emotions of people, especially during an epidemic period. Twitter, a microblogging platform, contains a sizable collection of datasets that illustrate a wide range of human emotions, including fear, happy, sad, anger, and joy etc. Sentiment analysis is crucial for gauging the consensus of the populace. This research can help us assess the potential impact of a pandemic on the general public's decision-making. © 2022 IEEE.

Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser

BackgroundInformation is essential for growth;without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of the certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons. AimsThis study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the coronavirus pandemic (COVID-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning.MethodologyThe hashtag ‘COVID-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a Twitter tokenizer, while TextBlob and Valence Aware Dictionary for Sentiment Reasoning (VADER) were used for text mining and sentiment analysis, respectively. Topic modelling was done with Latent Dirichlet Allocation and visualized with Multidimensional scaling.ResultsThe result of the VADER sentiment returned 39.8%, 31.3%, and 28.9%, positive, neutral, and negative sentiment, respectively, while the result of the TextBlob sentiment returned 46.0%, 36.7%, and 17.3%, neutral, positive, and negative sentiment, respectively.ConclusionWith all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of COVID-19. Also, know the opinion and challenges of people, then deal with the problem of misinformation. It is concluded that with popular belief a significant number of the populace regards COVID-19 as a virus that has come to stay, some believe it will eventually be conquered.

Big Data Analysis for Healthcare Application using Minhash and Machine Learning in Apache Spark Framework

Analysing data on a large scale is becoming important and engages in convincing many researchers to use new platforms and tools that can handle large amounts of data. In this article, we present new evaluation sentiment analysis for large-scale datasets of COVID-19 Vaccine Stance tweets and COVID-19 Tweets IEEE data port datasets in the Apache Spark data system. The Apache Spark Scalable Machine Learning Library (ML) is used. We designed hybrid minhash models from the library with four classification methods: Logistic Regression (LR), Naive Bayes, Support Vector Machine and Random Forest classifiers in a parallel and distributed manner. In addition, Minhash with locality Sensitive hashing (Minhash-LSH) is compared to Minhash-ML. Performance parameters such as user, system and real time, time consumed, and accuracy have been applied in the comparative analysis to analyse the behaviour of the classifiers in the AWS spark Cluster, Local Spark cluster and in conventional system. Results have indicated that the models in spark environment was extremely effective for processing large-dimension data, which cannot be processed with conventional implementation or take much time related to some algorithms. The proposed model achieves accuracy above 99% in case of Vaccine tweet dataset when classified with Minhash- RF and Minhash- LR classifiers. Also, 100% in case of COVID-19 Tweets Provided by IEEE data port when using Minhash-SVM, Minhash-RF and Minhash-LR classifiers. © 2022 IEEE.

The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study.

BACKGROUND: The COVID-19 pandemic exacerbated existing racial/ethnic health disparities in the United States. Monitoring nationwide Twitter conversations about COVID-19 and race/ethnicity could shed light on the impact of the pandemic on racial/ethnic minorities and help address health disparities. OBJECTIVE: This paper aims to examine the association between COVID-19 tweet volume and COVID-19 cases and deaths, stratified by race/ethnicity, in the early onset of the pandemic. METHODS: This cross-sectional study used geotagged COVID-19 tweets from within the United States posted in April 2020 on Twitter to examine the association between tweet volume, COVID-19 surveillance data (total cases and deaths in April), and population size. The studied time frame was limited to April 2020 because April was the earliest month when COVID-19 surveillance data on racial/ethnic groups were collected. Racially/ethnically stratified tweets were extracted using racial/ethnic group-related keywords (Asian, Black, Latino, and White) from COVID-19 tweets. Racially/ethnically stratified tweets, COVID-19 cases, and COVID-19 deaths were mapped to reveal their spatial distribution patterns. An ordinary least squares (OLS) regression model was applied to each stratified dataset. RESULTS: The racially/ethnically stratified tweet volume was associated with surveillance data. Specifically, an increase of 1 Asian tweet was correlated with 288 Asian cases (P<.001) and 93.4 Asian deaths (P<.001); an increase of 1 Black tweet was linked to 47.6 Black deaths (P<.001); an increase of 1 Latino tweet was linked to 719 Latino deaths (P<.001); and an increase of 1 White tweet was linked to 60.2 White deaths (P<.001). CONCLUSIONS: Using racially/ethnically stratified Twitter data as a surveillance indicator could inform epidemiologic trends to help estimate future surges of COVID-19 cases and potential future outbreaks of a pandemic among racial/ethnic groups.

Automated crisis content categorization for COVID-19 tweet streams

Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.