COVID-19: Boredom in the family

This research is focused on the subject of boredom in the families during the stay-at-home process forced by coronavirus disease 2019 pandemic. The literature on boredom was reviewed, and then the qualitative research was designed with the open-ended questions appropriate for the subject and purpose. The research was conducted between April 20 and 29, 2020, in Istanbul, Turkey, with the participation of 264 families. The most significant findings of the research showed that family members accustomed to active life experienced boredom more during the stay-at-home process, they utilized information technologies very often to overcome boredom, the importance of time spent at home increased, involuntary behaviors such as overeating and snacking became common, the livelihood difficulties and fear of unemployment increased boredom, nevertheless, no conflict occurred between the family members, and the process taught to be patient and strong. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Media Representation of China in COVID-19 Reports: Text Mining of the Language of New York Times

Textual mining, an application of natural language processing and analytical methods, effectively turns text into data, making machine analytics possible, especially in the field of textual framing and sentiment analysis, traditionally classified manually by researchers which unavoidably involves tremendous manpower and time. This study examines the themes and sentiments of news coverage of China against the backdrop of Covid-19 in the New York Times (NYT) ranging from January 2020 to January 2021 by employing the LDA topic modeling textbfand (Natural Language Toolkit) Vader SentimentAnalysertextbf. The result of a combination of quantitative and qualitative analysis reveals the foci and attitudes of NYT and highlights the selection, emphasis and exclusion practices in this Western media. The study thus broadens the scope of existing content analyses of the image of China and contributes to the exploratory application of text mining techniques in media and linguistic studies. © 2022 IEEE.

Analysis of the Potential Impact of Omicron Crises Using NLTK (Natural Language Toolkit)

It has been evident that there are several mutations in the Omicron virus in this variation, some of which are very serious. In contrast to another virus, initial investigation appears to suggest that variation has a sophisticated threat of reinfection. In all of South Africa’s outlying areas, the number of occurrences with this variation seems to grow. The Omicron Rising was compiled with the use of the Twitter API, tweeps, and the Python module. The Database will be updated regularly, and the same is used to speed on the latest developments in the COVID-19 virus. Furthermore, it has been observed that many statements classified as favorable by some techniques are sarcastic regarding new Omicron instances discovered throughout the world. Regardless of whether they use ‘positive’ language, their real meaning is negative. India appears to be the country with more tweets among those who have indicated a country. Of course, the study of the nation is skewed toward people from that country. Still, it seems natural that India has the most tweets since six instances of the Omicron variety were recently discovered in India. The tweets from India might result from concern over a potential Omicron disaster, identical to what transpired with the Delta Mutation in India in recent months. In this research study, quite exciting results are found, which will be very useful for identifying the user’s sentiment about the new variant. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Stock Price Prediction Using News Sentiment Analysis

In this era of digitization, tasks can be performed from anywhere in the world that previously required manual movement. It is the same for investing and trading in stocks. With the ease of investing and trading in stocks via the internet, a more extensive segment of society has started investing. The stock price depends on multiple factors such as politics, economics, war, society, and news sentiment. Therefore stocks are really hard to predict due to such vast dependencies. Stock markets are an important issue in the financial world. Prediction of stock prices during the global pandemic of Novel Coronavirus 2019 (COVID-19) can be very helpful to stakeholders. The attempt of predicting the stock prices have been made by previous researchers using sentimental news analysis through Support Vector Machine (SVM), Neural Network, and Naive Bayes. However, they have low accuracy, and some even claim that news is not a crucial governing factor for the stock price. This paper aims to predict the stock market prices through news sentimental analysis using techniques such as Long Short Term Memory and Artificial Neural Network against classifier models like Natural Language Toolkit, Valence Aware Dictionary for Sentiment Reasoning, Recurrent Neural Network for price prediction. S.Mohan [1] MAPE scores came out to be 1.17, 2.43 for RNN and RNN with news polarity for Facebook stock prices. Our results came out to be 1.21 and 1.94, slightly better results, thus showing optimism in the dependence of stock prices on the news. © 2022 IEEE.

Twitter Sentiment Analysis on Approval of Covaxin for Children in India

Covid-19 and its different variants are still a big issue the whole world is facing right now. At present different SARS-CoV-2 vaccines are playing vital role to combat the coronavirus. The objective of this paper is to perform sentiment analysis on approval of Bharat Biotech covaxin for emergency use for children. The presented paper emphasizes on the sentiment analysis of tweets of the microblogging site Twitter. Python programming language with Natural Language processing toolkit (NLTK), TextBlob library and tweepy twitter API are used for the process. Machine learning algorithms are used for the classification of tweeets. Graphical representation has been used for the representation of the data after sentiment analysis based on hashtags. © 2022 IEEE.

Sentiment Analysis about COVID-19 vaccine on Twitter data: Understanding Public Opinion

The COVID-19 pandemic has affected almost every segment of the worldwide population. Many vaccines were developed to cope with the ongoing COVID-19 outbreak. Indian government had initiated mass vaccination in early 2021 with objective of 100% vaccinated country. However, in spite of large number of awareness programs on the vaccination for the general public, still after more than one year 100% vaccination is not achieved. The reason for this is the hesitancy and lack of belief on the vaccination. The vaccination for children is now available and open for the general public. In order to boost the vaccination task, it is essential to understand the public opinion about the different vaccines available for adults and children. Social media platform, Twitter, is the most effective medium to know about the public perception and opinion about the vaccines. Therefore, an automated analysis of the public opinion using Twitter data is necessary to understand the mental thought process of public for the vaccine. The analysis allows the Indian governments to take some proactive measures to increase the public belief in the vaccine. Thus, the present study is intended to analyze the public sentiment on COVID-19 vaccination. 6000 real time tweets were fetched from the Twitter API with hashtag covid, covid19, vaccine, covaxin, covishield and childrenvaccine. The sentiments were analyzed and categorized into positive, negative and neutral sentiment by using TextBlob, Flair NLP, Stanza, and NLTK Vader algorithms. The performance of different algorithms was compared and evaluated. It was observed that NLTK Vader analyzed the public sentiment most correctly then the other algorithms. It was found that 34.210% sentiment were still negative about the vaccine which is a great matter of concern for the Indian governments. © 2022 IEEE.

Analysis of Sentiments on the Onset of COVID-19 Using Machine Learning Techniques

The novel coronavirus (COVID-19) pandemic has struck the whole world and is one of the most striking topics on social media platforms. Sentiment outbreak on social media enduring various thoughts, opinions, and emotions about the COVID-19 disease, expressing views they are feeling presently. Analyzing sentiments helps to yield better results. Gathering data from different blogging sites like Facebook, Twitter, Weibo, YouTube, Instagram, etc., and Twitter is the largest repository. Videos, text, and audio were also collected from repositories. Sentiment analysis uses opinion mining to acquire the sentiments of its users and categorizes them accordingly as positive, negative, and neutral. Analytical and machine learning classification is implemented to 3586 tweets collected in different time frames. In this paper, sentiment analysis was performed on tweets accumulated during the COVID-19 pandemic, Coronavirus disease. Tweets are collected from the Twitter database using Hydrator a web-based application. Data-preprocessing removes all the noise, outliers from the raw data. With Natural Language Toolkit (NLTK), text classification for sentiment analysis and calculate the score subjective polarity, counts, and sentiment distribution. N-gram is used in textual mining -and Natural Language Processing for a continuous sequence of words in a text or document applying uni-gram, bi-gram, and tri-gram for statistical computation. Term frequency and Inverse document frequency (TF-IDF) is a feature extraction technique that converts textual data into numeric form. Vectorize data feed to our model to obtain insights from linguistic data. Linear SVC, MultinomialNB, GBM, and Random Forest classifier with Tfidf classification model applied to our proposed model. Linear Support Vector classification performs better than the other two classifiers. Results depict that RF performs better.

Sentiment Analysis on COVID-19 Tweeter Dataset

Everyone is aware about coronavirus disease (in short it is COVID-19). The meaning of word COVID is defined as ‘CO’ stands for corona, ‘VI’ for virus, and ‘D’ for disease. It is an infectious disease caused by a newly discovered coronavirus which makes all countries on globe unstable. More than 206 countries is affected due to this COVID-19, and more than 110,00,000 people infected on the globe, and out of that more than 5,00,000 people died due to this incurable (till date no vaccination) disease. So that COVID-19 is declared pandemic. In this research, generic social media dataset related to COVID-19 is used for study and find sentiment analysis. In this article, twitter data collection, data preprocessing, and calculation of tweeter sentiment analysis were discussed in detail with respect to India and USA and whole world. Different Python libraries were discussed in this article. During 3 months, first 2 months, USA was more positive comparison to India and world. But, after lockdown in June, India is more positive compared to USA and world. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language

The paper has designed a dynamic dashboard that will show a summarized information of restaurants in Indonesia on four distinct metrics which are Food, Service, Ambience and Covid Safety. Each metrics shown will have their own ratings which shows the detailed score for each aspect of the restaurant. The data inside the dashboard have been developed by using semi supervised learning of aspect-based sentiment analysis approach. The idea is to analyze past reviews/comments of each restaurant in the current restaurant's online review platform and extract the sentiment as well as the aspect of each of the reviews. The restaurant lists and the reviews have been collected through web scraping method on one of the most used online review platforms in Indonesia which is Tripadvisor. Scraped data has been cleaned through several process of data pre-processing by utilizing Sastrawi and NLTK library for Indonesian languages. The machine learning tools that will extract the aspect and sentiments in every of the reviews will be built by applying Monkeylearn machine learning platform through APIs. Cleaned datasets have been imported into the platform for data annotations of model training to identify the set of words belongs in each aspect categories as well as their sentiment values. Although after reaching the end of the analysis, this paper has concluded that accuracy of the analysis may not be ideal due to lack of negative sentiment dataset being gathered which affects the model during the training process. In conclusion, the feature has successfully been built and implemented as well as deployed into a web server which supported by Ngrok services however, there are still more room for improvement regarding the analysis of the model. © 2021 IEEE.

Sentiment analysis tracking of COVID-19 vaccine through tweets.

Recent studies on the COVID-19 pandemic indicated an increase in the level of anxiety, stress, and depression among people of all ages. The World Health Organization (WHO) recently warned that even with the approval of vaccines by the Food and Drug Administration (FDA), population immunity is highly unlikely to be achieved this year. This paper aims to analyze people's sentiments during the pandemic by combining sentiment analysis and natural language processing algorithms to classify texts and extract the polarity, emotion, or consensus on COVID-19 vaccines based on tweets. The method used is based on the collection of tweets under the hashtag #COVIDVaccine while the nltk toolkit parses the texts, and the tf-idf algorithm generates the keywords. Both n-gram keywords and hashtags mentioned in the tweets are collected and counted. The results indicate that the sentiments are divided into positive and negative emotions, with the negative ones dominating.

Extractive Text Summarization for COVID-19 Medical Records

The method of reducing information from an original text document while maintaining the vital information is known as text summarizing. The amount of text data available has increased dramatically in recent years from a variety of sources. A large volume of text is an excellent source of information and knowledge of the source is essential for efficiently summarizing information that must be useful. Summarization facilitates the acquisition of vital and required information in a short period of time. Text summarization is required in a variety of domains, including news article summaries, email summaries and information summaries in the medical profession to track a patient's medical history for future treatment and so on. In summarization, there are two methods: extractive summarization and ive summarization. In this work, extractive summarization is used on the COVID-19 dataset. Different models and their results have been discussed. © 2021 IEEE.

Fine Grainded Sentiment Analysis on COVID-19 Vaccine

The most talked about topic of interest in the medical realm as of today, is the debate on the impact that COVID-19 vaccine has on individuals, and their response in encountering the virus. While there are quite a few vaccine variants that have been developed, there has always been a lingering ambiguity in declaring that an individual can be completely immune to the virus. There have been many studies whilom this cognition of analysing the sentiment perception of vaccines, however the data utilization from various sources and the apropos implementation using the language processing methodologies have lagged a great deal. This paper pivots on the data drawn from social media platforms, and optimizes the sentiments using the Natural Language processing Toolkit (NLTK). The process of word embedding, with TFIDF vectorizer commingled with data unsheathing through fine-grained sentiment analysis and machine learning algorithms such as Linear SVC, SVM and Naïve bayes on the covid19 dataset have aided in stratifying the public tweet sentiments based on their polarity, precision, recall, f1-score value and support. The simulations have been implemented using the lexicon, rubric-based analytical tool VADER (Valence Aware Dictionary and sentiment Reasoner) incorporated in Python specifically for optimized extraction of sentiments from data. © 2021 IEEE.

Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis.

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter's Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.