A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan's tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists' reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

Detecting Early Warning Indicators of Covid-19 Pandemic in the Context of United States: An Exploratory Data Analysis

This work aims to investigate if social media data, Twitter in particular can be used to detect early warning indicators of COVID-19 pandemic in the United States (US). To demonstrate the viability of this work, English tweets were collected with a hasghtag of COVID-19 related topics ranges from 12th March to end of April 2020. With the help of with N-gram language model and Term Frequency and Inverse Document Frequency (TF-IDF) significant bi-grams such as ("new york”), ("social, distancing”), ("stay, safe”), ("toilet, paper”), ("wash, hand”), ("tested, positive”), (look, like), ("front, line”), ("grocery, store”) etc. are extracted. Our analysis shows that, the natures of the bi-grams directly reflect the characteristics of the infection cases and are almost similarly distributed over different clusters. This study also reveals that, the tweets of ("new york”) increases with ("stay, home”), ("social, distancing”), ("stay, safe”), ("look, like”) and ("tested positive”);and decreases with ("toilet, paper”). Bi-grams with such relationships are recognized as indicators and are validated with the number of infection cases on each day. Results show that, social media data can project the actual scenario of infection curve and able to detect warning indicators once the pandemic is moderately recognized. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Study on the Analysis of Related Keywords on the Perception of Untact Coding Education in the Post-COVID Era Using Big Data Analysis

The novel infectious disease COVID-19, which started in December 2019, has plunged the world into a pandemic era. Accordingly, countries around the world are concerned about the spread of COVID-19, and have promoted large-scale gatherings and education through immigration control and social distancing. In particular, from March 15, 2020, elementary, secondary, high school, and university have conducted an unprecedented online opening of school in Korea's school education operation, and distance education classes have been conducted starting with the third year of middle and high schools across the country. In this process, students, faculty, and parents were faced with unexpected non-face-to-face distance education, and experienced confusion, tension, and uncertainty about the future society. Due to the spread of COVID-19, the online school curriculum has exposed the limitations and problems of the existing face-to-face school education system. In particular, coding education aimed at understanding and using software can interest students through face-to-face computer practice education. In addition, face-to-face education is important for non-IT majors to realize the importance of SW education and improve their convergence thinking skills. In this study, big data (SNS, Naver blog, youtube, google search keyword frequency, etc.) collected online to analyze the recognition and evaluation of untact (real-time online or pre-recorded video lecture) coding education was used as a text mining technique. Analysis was performed. ‘Coding education’, the keyword of this study, was selected as an analysis keyword, and data was collected through portals/SNSs of Google, Naver, Daum, and YouTube. In addition, after refining/morpheme analysis based on the collected list, word frequency, TF-IDF, N-gram, and topic modeling were analyzed through text mining, and matrix analysis, matrix chart, and sentiment analysis were performed. The results of this study will be used as basic data for college students’ coding education. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Classification of Indonesian Population's Level Happiness on Twitter Data Using N-Gram, Naïve Bayes, and Big Data Technology

—The level of happiness is one factor that influences social interaction in the community. Therefore, the population's happiness level within the current year has become an exciting concern to be studied. Since last year, the world has been facing a COVID-19 pandemic. COVID-19 pandemic dramatically affects the happiness level of the population from a social, economic, health, education, and tourism perspective. The various affected sectors cause different levels of emotional happiness in the community in terms of social interactions in opinions and issues on social media. In addition, the number of issues on social media induce a vast data warehouse and high complexity. Big Data is a science that handles large amounts of data, which is unmanageable using traditional data processing methods or techniques. Various companies, organizations, researchers, and academics practice Big Data to extract and analyze the necessary information. Big Data is a general term used for all data collection forms of vast and complex nature. The utilization of Big Data can be valuable for a better decision-making process. This study uses Big Data Technology to evaluate the Indonesian population's happiness level on Twitter data. Method classified and technique using the N-Gram, Naïve Bayes, and Laplacian Smoothing Technique. The emotion in this research is classified into two aspects: happy and unhappy emotions. A total of 4.306.581 tweet data is classified;the obtained results revealed 39,4% happy emotion and 60,6% unhappy emotion. © 2022, International Journal on Advanced Science, Engineering and Information Technology. All Rights Reserved.

Lexical modeling and weighted matrices for analyses of COVID-19 outbreak

The most dangerous and infectious disease, COVID-19, affecting millions of people is by an enveloped RNA virus known as SARS-COV-2 or Coronavirus, and the disease is unknown before the epidemic commenced in Wuhan, China, in December 2019. Many researchers are busy finding the vaccine for the pandemic. Here, we analyze the diagnostic methods by using mathematical modeling. The majority probable corona patient category with an enhanced AUC characterizes the SVM’s optimal diagnostics model in this chapter. Experimental and computational analyses demonstrate that the diagnosis of potentially COVID-19 can be supported by adopting ML algorithms that learn linguistic diagnostics from the interpretation of elderly persons. Highlight the collection of significant semantic, lexical, and top n-gram properties with the better ML method to estimate diseases. But diagnostics methods must be trained on massive datasets, leading to improved AUC and medical diagnoses of COVID-19 probability. A significant use resulting from mathematical modeling is that it claims transparency and accurateness about our model. These techniques can help in decision-making by useful predictions about substantial issues such as treatment protocols and interfere and minimize the spread of COVID-19. © 2022 Elsevier Inc. All rights reserved.

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.

Desinformación rusa en Europa del Este. Marcos mediáticos de la vacunación en ro.sputnik.md

The news site ro.sputnik.md is the Romanian language version of the Sputnik news website platform, owned by the Russian government, one of the main channels used by the Kremlin to disseminate mis- and disinformation across Russian borders. To map the media frames and the lexical and discursive constructions, the research proposes a mixed methods content-based approach, where automated text analysis (frequency, co-occurrence, n-grams) is combined with thematic and discourse analysis. Six emphasis frames are identified in the corpus (N=1,165): Superiority of the Russian Sputnik V Vaccine, Fatal/Side Effects of EU Authorized Vaccines, Limitations of Individual Rights and Freedoms, EU and/or Romanian Authorities' Struggle, Children and Teenagers' Protection, and Big Pharma Conspiracy. The findings show that specific discursive patterns are associated with the negative news value: death, side effects (blood clot, thrombosis, coagulation), restrictions, and interdictions or warnings (serious, risk, negative, panic, etc.), while the conflict news value is associated with warfare vocabulary (defense, threat, battle, fire, gunpowder, etc.);and eliteness, with well-known actors (state leaders, European leaders, famous "conspirators") and countries (powerful international actors, meaningful neighbours).

AI-Controlled Robot Masks: Resisting Patriarchal Oppression

Using computational techniques to foster new empathetic relationships between human bodies and the space around them, Behnaz Farahi, Assistant Professor in the Department of Design at the California State University in Long Beach, presents some of the concepts and events that have inspired her research and focuses on a recent project for an interactive niqab. Copyright © 2022 John Wiley & Sons, Ltd.

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.

Ensemble Model Ransomware Classification: A Static Analysis-based Approach

The growth of malware attacks has been phenomenal in the recent past. The COVID-19 pandemic has contributed to an increase in the dependence of a larger than usual workforce on digital technology. This has forced the anti-malware communities to build better software to mitigate malware attacks by detecting it before they wreak havoc. The key part of protecting a system from a malware attack is to identify whether a given file/software is malicious or not. Ransomware attacks are time-sensitive as they must be stopped before the attack manifests as the damage will be irreversible once the attack reaches a certain stage. Dynamic analysis employs a great many methods to decipher the way ransomware files behave when given a free rein. But, there still exists a risk of exposing the system to malicious code while doing that. Ransomware that can sense the analysis environment will most certainly elude the methods used in dynamic analysis. We propose a static analysis method along with machine learning for classifying the ransomware using opcodes extracted by disassemblers. By selecting the most appropriate feature vectors through the tf-idf feature selection method and tuning the parameters that better represent each class, we can increase the efficiency of the ransomware classification model. The ensemble learning-based model implemented on top of N-gram sequence of static opcode data was found to improve the performance significantly in comparison to RF, SVN, LR, and GBDT models when tested against a dataset consisting of live encrypting ransomware samples that had evasive technique to dodge dynamic malware analysis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme.

The current population worldwide extensively uses social media to share thoughts, societal issues, and personal concerns. Social media can be viewed as an intelligent platform that can be augmented with a capability to analyze and predict various issues such as business needs, environmental needs, election trends (polls), governmental needs, etc. This has motivated us to initiate a comprehensive search of the COVID-19 pandemic-related views and opinions amongst the population on Twitter. The basic training data have been collected from Twitter posts. On this basis, we have developed research involving ensemble deep learning techniques to reach a better prediction of the future evolutions of views in Twitter when compared to previous works that do the same. First, feature extraction is performed through an N-gram stacked autoencoder supervised learning algorithm. The extracted features are then involved in a classification and prediction involving an ensemble fusion scheme of selected machine learning techniques such as decision tree (DT), support vector machine (SVM), random forest (RF), and K-nearest neighbour (KNN). all individual results are combined/fused for a better prediction by using both mean and mode techniques. Our proposed scheme of an N-gram stacked encoder integrated in an ensemble machine learning scheme outperforms all the other existing competing techniques such unigram autoencoder, bigram autoencoder, etc. Our experimental results have been obtained from a comprehensive evaluation involving a dataset extracted from open-source data available from Twitter that were filtered by using the keywords "covid", "covid19", "coronavirus", "covid-19", "sarscov2", and "covid_19".

Data Mining on COVID-19 Vaccines: Side Effects.

COVID-19 is a pandemic disease affecting billions of people worldwide. Taking vaccines is a most effective approach to gain fully control. Thanks to the coordinated efforts from all over the world, several brands of vaccines targeting COVID-19 have passed through clinical trials and been brought to the public. Growing numbers of people are taking vaccines and share their feedback on social media, mostly on Twitter. In this study, we used Twitter data to analyze the side effects on each individual and quantify these side effects in a brand-wise and country-wise manner. Based on Twitter data, we found that the United States has the largest number of people getting vaccinated, Pfizer is the most widely used vaccine brand around the world and the most frequent side effect is cold. From our analysis, the side effects of vaccines are under controllable and are acceptable, and everyone can join the vaccinated camping without hesitation.

n-Gram based language processing using Twitter dataset to identify COVID-19 patients.

Due to the rapid growth of electronic documents, e.g., tweets, blogs, Facebook posts, snaps in different languages that use the same writing script, language categorization, and processing have great importance. For instance, to identify COVID-19 positive patients or people's emotions on COVID-19 pandemic from tweets written in 35 different languages faster and accurate, language categorization and processing of tweets is significantly essential. Among many language categorization and processing techniques, character and word n-gram based techniques are very popular and simple but very efficient for categorizing and processing both short and large documents. One of the fundamental problems of language processing is the efficient use of memory space in implementing a technique so that a vast collection of documents can be easily categorized and processed. In this paper, we introduce a framework that categorizes the language of tweets using n-gram based language categorization technique and further processes the tweets using the machine-learning approach, Linear Support Vector Machine (LSVM), that may be able to identify COVID-19 positive patients. We evaluate and compare the performance of the proposed framework in terms of language categorization accuracy, precession, recall, and F-measure over n-gram length. The proposed framework is scalable as many other applications that involve extracting features and classifying languages collected from social media, and different types of networks may use this framework. This proposed framework, also being a part of health monitoring and improvement, tends to achieve the goal of having a sustainable society.

A Sentiment Analysis Approach to Predict an Individual's Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia.

In March 2020, the World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) as a pandemic, which affected all countries worldwide. During the outbreak, public sentiment analyses contributed valuable information toward making appropriate public health responses. This study aims to develop a model that predicts an individual's awareness of the precautionary procedures in five main regions in Saudi Arabia. In this study, a dataset of Arabic COVID-19 related tweets was collected, which fell in the period of the curfew. The dataset was processed, based on several machine learning predictive models: Support Vector Machine (SVM), K-nearest neighbors (KNN), and Naïve Bayes (NB), along with the N-gram feature extraction technique. The results show that applying the SVM classifier along with bigram in Term Frequency-Inverse Document Frequency (TF-IDF) outperformed other models with an accuracy of 85%. The results of awareness prediction showed that the south region observed the highest level of awareness towards COVID-19 containment measures, whereas the middle region was the least. The proposed model can support the medical sectors and decision-makers to decide the appropriate procedures for each region based on their attitudes towards the pandemic.