Visual analysis of mask sales data based on crawler

The outbreak of COVID-19 makes people feel distant from each other, and masks have become one of the indispensable articles in People's Daily life. At present, there are many brands of masks with various types and uneven quality. In order to understand the current market of masks and the sales of different brands, users can choose masks with perfect quality. This paper uses Python web crawler technology, based on the input of the word "mask", crawl JD website sales data, through data visualization technology drawing histogram, pie chart, the word cloud, etc., for goods compared with the relationship between price, average price of all brands, brands, average distribution of analysis and evaluation of user information, In this way, the sales situation, price distribution and quality evaluation of each store of the product can be visually displayed. At the same time, it also provides some reference for other users who need to buy the product. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

Strategic Airline Service Review in the Time of the COVID-19 Crisis

This study extracted airline data from several online source to examine operational and service strategy of the airline industry during the COVID-19 pandemic. The results have suggested that airlines were losing market shares in this pandemic situation except for those with high assets. In addition, this study utilizes text analytics techniques to provide insight into service characteristics that distinguish positive from negative reviews. The results suggest that satisfied travelers are demanding services with high empathy and responsiveness, while negative reviewers frequently complain about insufficient operational aspects such as ground operations, mishandled baggage, system glitches, and staff management on handling cancellation. Copyright © 2022, IGI Global.

Analysis of the Covid-19 Process in Terms of Health Managers

In this study, sentiment analysis was conducted on the data of the Covid-19 epidemic process from the official twitter account of the Republic of Turkey Fahrettin Koca, Minister of Health, @drfahrettinkoca (SO) and the Twitter account of the @WHO (World Health Organization). First of all, twitter data was obtained and necessary arrangements were made for analysis. Then, tweets were shown with a word cloud and it was determined which words were used more frequently. Afterwards, sentiment analysis was performed on the data using the TextBlob library. In addition, it has been found out which subjects are focused on tweets sent from SO and @WHO (World Health Organization) accounts with the LDA algorithm. It has been seen that positive tweets were sent from both accounts, giving positive messages to the society. © 2022 IEEE.

Analysis of Research Paper Titles Containing Covid-19 Keyword Using Various Visualization Techniques

It’s been around two years from the outbreak of the coronavirus, thus labeled as Covid-19, and there has been an explosion of literature being published by research scholars related to work done on Covid-19. Covid-19 as a keyword has been mentioned in the titles of most of these papers. It was thought to analyse the number of papers and the titles of papers which include Covid-19 in the title of the research papers. The various combinations of other words like, prefixes, suffixes, N-gram combinations with the keyword Covid- 19 in the titles of these papers were also analysed. The research publication repositories analysed were: IEEE Explore, ACM Digital Library, Semantic Scholar, Google Scholar, Cornel University etc. The domains of research publication title analysis were restricted to computer science/computer engineering related papers. As the term labeling the corona virus outbreak as Covid-19 was labeled in 2020, the timeline of title analysis was restricted from 2019 till December 2021. The term Covid-19 is also one of the most searched terms in most of these research repositories as is evident from the search suggestions offered by them. Considering the usefulness of Bag of Words and N Gram algorithm in analytics and data visualization, a methodology is proposed and implemented based on bag of words algorithm to do prefix and suffix words analysis. This methodology is working correctly to state different prefix and suffix words used by various researchers to demonstrate significance of their titles. Methodology based on N Gram analysis is found effective to find topic on which most of the researchers have done work. Word Clouds are generated to demonstrate different buzz words used by researchers in their respective paper titles. These are useful for providing visualization of the data if it is in big size. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Fake News Detection on Social Media

Since the outbreak of COVID-19, social media plays an important role to circulate pandemic news around the world. Some malevolent users may take an advantage of this and spread fake news to attract people for business and research purposes. In this paper, we take an approach by applying existing machine learning algorithms to detect fake news in social media and show a comparison of their performances. In our study, the support vector classifier (SVC) outperforms the rest of the classifiers based on different statistical metrics. Therefore, the SVC classifier has been considered as our proposed classifier model to identify fake COVID-19 news in social media. Two word clouds have also been generated based on the appearance of words in the news that shows an insignificant difference between true and fake news. © 2021 IEEE.

Twitter Sentiment Analysis: Caribbean Prime Ministers Response to COVID-19 Pandemic

In today's world, millions of people use social networking and microblogging sites every day to share their views, opinions, and emotions in their daily lives. These sites can become an invaluable source for data mining and can be used effectively to evaluate people's opinion on a product, an entity or perhaps topics of interest. Sentiment Analysis, as it is called, allows us to determine whether the opinions, mood, views, or attitude in a text is either 'positive', 'negative', or 'neutral'. The focus of this study was to analyze the tweets of the top ten English-speaking Caribbean Prime Ministers on Twitter to determine how effective they were in reducing the spread of the COVID-19 outbreak in their territories. The research results provided clear evidence that the negative sentiment towards the virus by the Caribbean leaders was a contributing factor in reducing the number of cases and deaths during the first five months of COVID-19 in the region. The results also found that a correlation exists between the prime ministers' social network and their effectiveness in managing the virus. In addition, the words expressed by the prime ministers in reference to COVID-19 were clear and practical therefore making it easier for the prime ministers to implement strict measures to control the spread of the virus in the region. © 2021 IEEE.

Sentiment Analysis of COVID-19 Vaccines from Indonesian Tweets and News Headlines using Various Machine Learning Techniques

COVID-19 vaccine is a hot topic in online platforms due to the ongoing pandemic. Most studies on sentiment analysis of COVID-19 vaccines on Indonesian social media posts only used one or two classifiers with few modifications. This research investigated sentiment analysis using seven machine learning techniques on Twitter dataset in which the one with the highest evaluation value will be used to predict on other unlabeled Twitter datasets as well as news headlines dataset. The same classifier is also used to build a visualization dashboard that reflect the result of the sentiments. The result from the sentiment classification is then used to identify the topics, by using word cloud. The experiment revealed that SVM classifier has the highest accuracy and micro average F1-measure, which is 84% and 0.76. This classifier managed to capture similar patterns of sentiments in Twitter and news headlines datasets, which is dominated by neutral sentiment. Some of the topics from each sentiment, managed to reflect the real condition when the datasets were collected. © 2021 IEEE.

Mediated Solidarity and Community Resilience on Twitter during Covid-19 Pandemic in Indonesia

The COVID-19 pandemic makes it difficult for people to carry out their activities, especially those who work in the informal sector. The lower middle class has experienced obstacles in carrying out work and lost income. Social media is one of the media that mediates solidarity movements between communities in each region in Indonesia. This study aims to explain how Twitter mediates the social solidarity movement amid the COVID-19 pandemic in Indonesia. This research is conducted through Twitter analytics processed using machine learning. The authors collected data between March 1 - December 1 of 2020 and analyzed them using machine learning, including polarity sentiment, emotion sentiment, topic in the word cloud, and social network analysis. The findings show that conversations on Twitter concerning solidarity are not just regular conversations. Mediated solidarity conversations on Twitter can influence another solidarity movement within the same hashtag or word cloud topic that reflects society emotions in supporting each other. A positive sentiment regarding these conversations is also relevant with the SNA, showing no contradictions. All these conversations inspired each other to be strong and unify. These public conversations on Twitter indicate the Indonesian community resilience in facing emergency conditions. © 2021 IEEE.

Twitter Sentiment Analysis on COVID-19

The COVID-19 pandemic has been a trending topic on social media since it first started in December 2019. This pandemic originated in the city of Wuhan in China. India was vastly affected by this pandemic due to its large population. However, due to its vast population, India has a large number of social media users, which can provide crucial insight into people's perspectives on topics related to the pandemic. This paper uses natural language processing and sentiment analysis on the posts created by users on the social media platform of Twitter. The study uses APIs and keywords to get the data to analyze and understand the emotions of the tweets linked to topics like oxygen, vaccine, beds, and lockdown in the times of COVID-19. The results and observations are presented using various graphs, charts, and word clouds. This paper aims to help the government, researchers, and frontline workers to get an insight into the sentiment on social media about various topics concerning the covid-19 pandemic. © 2021 IEEE.

Sentiment Analysis of Tweets during COVID-19 Pandemic Using BLSTM

social media is the new way for people to express and share their thoughts and it plays a huge role in spreading the anxiety widely during the pandemics and Twitter is one of the social media channels with high number of users and daily tweets. Clarifying and understanding what people thinks and their shared opinions during such hard times can reduce the burden on the health systems and entities and redirects the concerned entities by highlighting the areas to spread the awareness in. The aim of this study is to analyze and assess the sentiment of the Tweets shared during the COVID-19 pandemic and testing the Bidirectional Long Term Short Memory (BLTSM) of Recurrent Neural Network (RNN) in predicting the sentiment class which are Negative, Positive and Neutral. The results show slight difference between the positive and negative tweets which needs an attention to spread the awareness and hence, reduce the negativity. Furthermore, the BLSTM predicted the sentiment classes (Negative, Positive and Neutral) and obtained 86.15% accuracy rate. The high accuracy concludes that BLSTM can adapt and predict the sentiment of a text. © 2021 IEEE.