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1.
Expert Systems with Applications ; JOUR: 119262,
Article in English | ScienceDirect | ID: covidwho-2104915

ABSTRACT

The onset of the COVID-19 pandemic has changed consumer usage behavior towards mobile payment (m-payment) services. Consumer usage behavior towards m-payment services continues to increase due to access to usage experiences shared through online consumer reviews (OCRs). The proliferation of massive OCRs, coupled with quick and effective decisions concerning the evaluation and selection of m-payment services, is a practical issue for research. This paper develops a novel decision evaluation model that integrates OCRs and multi-attribute decision-making (MADM) with probabilistic linguistic information to identify m-payment usage attributes and utilize these attributes to evaluate and rank m-payment services. First and foremost, the attributes of m-payment usage discussed by consumers in OCRs are extracted using the Latent Dirichlet Allocation (LDA) topic modeling approach. These key attributes are used as the evaluation scales in the MADM. Based on an unsupervised sentiment algorithm, the sentiment scores of the text reviews regarding the attributes are calculated. We convert the sentiment scores into probabilistic linguistic elements based on the probabilistic linguistic term set (PLTS) theory and statistical analysis. Furthermore, we construct a novel technique known as probabilistic linguistic indifference threshold-based attribute ratio analysis (PL-ITARA) to discover the weight importance of the usage attributes. Subsequently, the positive and negative ideal-based PL-ELECTRE I methodology is proposed to evaluate and rank m-payment services. Finally, a case study on selecting appropriate m-payment services in Ghana is examined to authenticate the validity and applicability of our proposed decision evaluation methodology.

2.
Computers & Security ; JOUR: 103008,
Article in English | ScienceDirect | ID: covidwho-2104679

ABSTRACT

Many researchers have studied non-expert users’ perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users’ tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results confirm the multi-faceted nature of non-expert users’ perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives.

3.
CAAI Transactions on Intelligence Technology ; JOUR
Article in English | Web of Science | ID: covidwho-2087347

ABSTRACT

The COVID-19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India-specific COVID-19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short-term memory, Bi-directional long short-term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.

4.
J Med Internet Res ; 24(11): e42261, 2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2089646

ABSTRACT

BACKGROUND: Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (eg, Twitter, Reddit, and Facebook) for user-created content from which they can gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses a negative sentiment toward vaccination, it can give direct insight into the reasons for vaccine hesitancy. OBJECTIVE: This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover the reasons for vaccine hesitancy among the negative tweets in the Serbian language. METHODS: To study the attitudes and beliefs behind vaccine hesitancy, we collected 2 batches of tweets that mention some aspects of COVID-19 vaccination. The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9%) from the manually annotated data set and 1516 (46.1%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. RESULTS: The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. CONCLUSIONS: This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process.

5.
Revista Espanola De Sociologia ; JOUR(4), 31.
Article in English | Web of Science | ID: covidwho-2082593

ABSTRACT

Hashtag research has established itself as a relevant research field, with various studies having analysed this polysemic collector in crisis and media events. Hashtags are used in social media, most specifically on Twitter. Further, between 2020 and 2021, hashtag studies linked to the COVID-19 pandemic have emerged. Accordingly, this study aimed to analyse the content of tweets during the first phase of the COVID-19 pandemic (March 4-11, 2020) that included the hashtag #Covid-19 in three different languages: Italian, Spanish, and French. For these analyses, we used emotional text mining. The goal of this study was to reconstruct the representation of the pandemic, of containment measures, and of Europe in tweets. We discussed the prevailing attitude towards Europe in times of crisis.

6.
Concurrency and Computation: Practice and Experience ; JOUR
Article in English | Web of Science | ID: covidwho-2082435

ABSTRACT

Many researchers in various disciplines have focused on extracting meaningful information from social media platforms in recent years. Identification of behaviors and emotions from user posts is examined under the heading of sentiment analysis (SA) studies using the natural language processing (NLP) techniques. In this study, a novel TCNN-Bi-LSTM model using the two-stage convolutional neural network (TCNN) and bidirectional long short-term memory (Bi-LSTM) architectures was proposed. While TCNN layers enable the extraction of strong local features, the output of these layers feeds the Bi-LSTM model that remembers forward-looking information and capture long-term dependencies. In this study, first, preprocessing steps were applied to the raw dataset. Thus, strong features were extracted from the obtained quality dataset using the FastText word embedding technique that pre-trained with location-based and sub-word information features. The experimental results of the proposed method are promising compared to the baseline deep learning and machine learning models. Also, experimental results show that while the FastText data embedding technique achieves the best performance compared to other word embedding techniques in all deep learning classification models, it has not had the same outstanding success in machine learning models. This study aims to investigate the sentiments of tweets about the COVID-19 vaccines and comments on these tweets among Twitter users by using the power of Twitter data. A new dataset collected from Twitter was constructed to be used in experimental results. This study will facilitate detecting inappropriate, incomplete, and erroneous information about vaccination. The results of this study will enable society to broaden its perspective on the administered vaccines. It can also assist the government and healthcare agencies in planning and implementing the vaccination's promotion on time to achieve the herd immunity provided by the vaccination.

7.
Sensors (Basel) ; 22(21)2022 Oct 24.
Article in English | MEDLINE | ID: covidwho-2081962

ABSTRACT

This paper proposes a methodology for sentiment analysis with emphasis on the emotional aspects of people visiting the Herculaneum Archaeological Park in Italy during the period of the COVID-19 pandemic. The methodology provides a valuable means of continuous feedback on perceived risk of the site. A semantic analysis on Twitter text messages provided input to the risk management team with which they could respond immediately mitigating any apparent risk and reducing the perceived risk. A two-stage approach was adopted to prune a massively large dataset from Twitter. In the first phase, a social network analysis and visualisation tool NodeXL was used to determine the most recurrent words, which was achieved using polarity. This resulted in a suitable subset. In the second phase, the subset was subjected to sentiment and emotion mapping by survey participants. This led to a hybrid approach of using automation for pruning datasets from social media and using a human approach to sentiment and emotion analysis. Whilst suffering from COVID-19, equally, people suffered due to loneliness from isolation dictated by the World Health Organisation. The work revealed that despite such conditions, people's sentiments demonstrated a positive effect from the online discussions on the Herculaneum site.


Subject(s)
COVID-19 , Social Media , Humans , Pandemics , Emotions , Attitude , Perception
8.
JMIR Med Inform ; 10(11): e37945, 2022 Nov 10.
Article in English | MEDLINE | ID: covidwho-2079974

ABSTRACT

BACKGROUND: The increasing availability of "real-world" data in the form of written text holds promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information, allowing the capture of lived experiences through a broad range of different sources of information (eg, content and emotional tone). Interviews are the "gold standard" for gaining qualitative insights into individual experiences and perspectives. However, conducting interviews on a large scale is not always feasible, and standardized quantitative assessment suitable for large-scale application may miss important information. Surveys that include open-text assessments can combine the advantages of both methods and are well suited for the application of natural language processing (NLP) methods. While innovations in NLP have made large-scale text analysis more accessible, the analysis of real-world textual data is still complex and requires several consecutive steps. OBJECTIVE: We developed and subsequently examined the utility and scientific value of an NLP pipeline for extracting real-world experiences from textual data to provide guidance for applied researchers. METHODS: We applied the NLP pipeline to large-scale textual data collected by the Swiss Multiple Sclerosis (MS) registry. Such textual data constitute an ideal use case for the study of real-world text data. Specifically, we examined 639 text reports on the experienced impact of the first COVID-19 lockdown from the perspectives of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the "Linguistic Inquiry and Word Count" software. It consists of the following 5 interconnected analysis steps: (1) text preprocessing; (2) sentiment analysis; (3) descriptive text analysis; (4) unsupervised learning-topic modeling; and (5) results interpretation and validation. RESULTS: A topic modeling analysis identified the following 4 distinct groups based on the topics participants were mainly concerned with: "contacts/communication;" "social environment;" "work;" and "errands/daily routines." Notably, the sentiment analysis revealed that the "contacts/communication" group was characterized by a pronounced negative emotional tone underlying the text reports. This observed heterogeneity in emotional tonality underlying the reported experiences of the first COVID-19-related lockdown is likely to reflect differences in emotional burden, individual circumstances, and ways of coping with the pandemic, which is in line with previous research on this matter. CONCLUSIONS: This study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes to both the dissemination of NLP techniques in applied health sciences and the identification of previously unknown experiences and burdens of persons with MS during the pandemic, which may be relevant for future treatment.

9.
2022 International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2078199

ABSTRACT

COVID-19 pandemic had shaken societies in the world at various levels. Most organizations were forced to adopt the Work from Home strategy from 2020 onwards. This had reportedly increased internet and social media usage among the people. India was also not different from the rest of the world. The nation was affected by the second and third waves of the COVID-19 pandemic. Among them, Kerala and Tamil Nadu were some of the affected states in the nation;with Tamil Nadu reporting fewer positive cases and Test Positive Rate (TPR) compared to that Kerala. This paper focuses on the attitude of people towards the pandemic by extracting tweets from Twitter. A comparative study between the two states was conducted. After analyzing the rumors and information shared by the people through tweets, it has been identified that the people of Tamil Nadu were a bit more conscious than the people in Kerala. The study also reveals that people in Kerala were not vigilant during the Omicron wave as compared to the time before. © 2022 IEEE.

10.
Proc Natl Acad Sci U S A ; 119(43): e2210988119, 2022 10 25.
Article in English | MEDLINE | ID: covidwho-2077261

ABSTRACT

Climate change mitigation has been one of the world's most salient issues for the past three decades. However, global policy attention has been partially diverted to address the COVID-19 pandemic for the past 2 y. Here, we explore the impact of the pandemic on the frequency and content of climate change discussions on Twitter for the period of 2019 to 2021. Consistent with the "finite pool of worry" hypothesis both at the annual level and on a daily basis, a larger number of COVID-19 cases and deaths is associated with a smaller number of "climate change" tweets. Climate change discussion on Twitter decreased, despite 1) a larger Twitter daily active usage in 2020 and 2021, 2) greater coverage of climate change in the traditional media in 2021, 3) a larger number of North Atlantic Ocean hurricanes, and 4) a larger wildland fires area in the United States in 2020 and 2021. Further evidence supporting the finite pool of worry is the significant relationship between daily COVID-19 cases/deaths on the one hand and the public sentiment and emotional content of climate change tweets on the other. In particular, increasing COVID-19 numbers decrease negative sentiment in climate change tweets and the emotions related to worry and anxiety, such as fear and anger.


Subject(s)
COVID-19 , Social Media , Anxiety/epidemiology , COVID-19/epidemiology , Emotions , Humans , Pandemics , United States
11.
Tec Empresarial ; 16(3):72-91, 2022.
Article in English | Web of Science | ID: covidwho-2072332

ABSTRACT

This research evaluates response strategies to crisis communication, in terms of perceived risks, in organizations during the Covid-19 pandemic by studying the main discussion topics in social media. The data was collected from Twitter between March and April 2020. By using big data software, a total number of 3559255 tweets in different languages were extracted worldwide from Twitter API of popular hashtags on the Covid-19 pandemic. The data processing was carried out through the association of terms in order to identify patterns and relationships in the discussion topics. The results indicate that the relationships of the terms "crisis" and "risks" were statistically significant with seven important topics for businesses, users, and consumers: "business", "economic and financial", "social"," health"," work"," family" and "government";and in turn these seven topics are related to other terms related to the impact of the crisis, the response to the crisis, aid, the watch out, and support. This research has implications for the situational crisis communication theory by showing that in situations with high perceived risk, such as the Covid-19 pandemic crisis, the use of crisis response strategies predominates in organizations. This research also has implications for managers who can use crisis response strategies to rebuild their reputation and avoid market losses, thus helping to reduce the effects of unpredictable crisis situations.

12.
2022 Ieee Conference on Evolving and Adaptive Intelligent Systems (Ieee Eais 2022) ; 2022.
Article in English | Web of Science | ID: covidwho-2070337

ABSTRACT

Natural Language Processing (NLP) can analyze and classify the growing number of expressed opinions and feelings of online texts and quickly get the required feedback. The technique of automatically labeling a textual document with the most appropriate collection of labels is known as text classification, whereas supervised text classifiers require extensive human expertise and labeling efforts. This paper seeks to build a multi-labeled Arabic dataset by labeling an Arabic Covid-19 Tweet to two groups based on their lexical features: related topic and associated sentiment. An extensive dataset was created from Twitter posts to achieve this purpose. There are over 32k multi-labeled tweets in the dataset. The dataset will be made freely available to the Arabic computational linguistics research community. This work used both traditional machine learning approaches and a deep-learning approach to investigate this dataset's performance. This paper demonstrates that traditional ML approaches provide higher accuracy with almost stable performance when experienced on the Twitter dataset for sentiment analysis and topic classification.

13.
Ieee Access ; 10:103176-103186, 2022.
Article in English | Web of Science | ID: covidwho-2070270

ABSTRACT

In large MOOC cohorts, the sheer variance and volume of discussion forum posts can make it difficult for instructors to distinguish nuanced emotion in students, such as engagement levels or stress, purely from textual data. Sentiment analysis has been used to build student behavioral models to understand emotion, however, more recent research suggests that separating sentiment and stress into different measures could improve approaches. Detecting stress in a MOOC corpus is challenging as students may use language that does not conform to standard definitions, but new techniques like TensiStrength provide more nuanced measures of stress by considering it as a spectrum. In this work, we introduce an ensemble method that extracts feature categories of engagement, semantics and sentiment from an AdelaideX student dataset. Stacked and voting methods are used to compare performance measures on how accurately these features can predict student grades. The stacked method performed best across all measures, with our Random Forest baseline further demonstrating that negative sentiment and stress had little impact on academic results. As a secondary analysis, we explored whether stress among student posts increased in 2020 compared to 2019 due to COVID-19, but found no significant change. Importantly, our model indicates that there may be a relationship between features, which warrants future research.

14.
Cureus ; 14(9): e29323, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-2072210

ABSTRACT

Background Clinically extremely vulnerable (CEV) individuals have a significantly higher risk of morbidity and mortality from coronavirus disease 2019 (COVID-19). This high risk is due to predispositions such as chronic obstructive pulmonary disease (COPD), diabetes mellitus, hypertension, smoking, or extreme age (≥75). The initial COVID-19 preventive measures (use of face masks, social distancing, social bubbles) and vaccine allocation prioritized this group of vulnerable individuals to ensure their continued protection. However, as countries start relaxing the lockdown measures to help prevent socio-economic collapse, the impact of this relaxation on CEVs is once again brought to light. In this study, we set out to understand the impact of policy changes on the lives of CEVs by analyzing Twitter data with the hashtag #highriskcovid used by many high-risk individuals to tweet about and express their opinions and feelings. Methodology Tweets were extracted from the Twitter API between March 01, 2022, and April 21, 2022, using the Twarc2 tool. Extracted tweets were in English and included the hashtag #highriskcovid. We evaluated the most frequently used words and hashtags by calculating term frequency-inverse document frequency, and the location of tweets using the tidygeocoder package (method = osm). We also evaluated the sentiments and emotions depicted by these tweets using the National Research Council sentiment lexicon of the Syuzhet package. Finally, we used the latent Dirichlet allocation algorithm to determine relevant high-risk COVID-19 themes. Results The vast majority of the tweets originated from the United States (64%), Canada (22%), and the United Kingdom (4%). The most common hashtags were #highriskcovid (25.5%), #covid (6.82%), #immunocompromised (4.93%), #covidisnotover (4.0%), and #Maskup (1.40%), and the most frequently used words were immunocompromised (1.64%), people (1.4%), disabled (0.97%), maskup (0.85%), and eugenics (0.85%). The tweets were more negative (19.27%) than positive, and the most expressed negative emotions were fear (13.62%) and sadness (12.47%). At the same time, trust was the most expressed positive emotion and was used in relation to belief in masks, policies, and health workers to help. Finally, we detected frequently co-tweeted words such asmass and disaster, deadly and disabling, high and risk, public and health, immunocompromised and people, mass and disaster, and deadly and disabling. Conclusions The study provides evidence regarding the concerns and fears of high-risk COVID-19 groups as expressed via social media. It is imperative that further policies be implemented to specifically protect the health and mental wellness of high-risk individuals (for example, incorporating sentiment analyses of high-risk COVID-19 individuals such as this paper to inform the evaluation of already implemented preventive measures and policies). In addition, considerable work needs to be done to educate the public on high-risk individuals.

15.
Int J Environ Res Public Health ; 19(20)2022 Oct 18.
Article in English | MEDLINE | ID: covidwho-2071482

ABSTRACT

Doctor-patient relationships (DPRs) in China have been straining. With the emergence of the COVID-19 pandemic, the relationships and interactions between patients and doctors are changing. This study investigated how patients' attitudes toward physicians changed during the pandemic and what factors were associated with these changes, leading to insights for improving management in the healthcare sector. This paper collected 58,600 comments regarding Chinese doctors from three regions from the online health platform Good Doctors Online (haodf.com, accessed on 13 October 2022). These comments were analyzed using text mining techniques, such as sentiment and word frequency analyses. The results showed improvements in DPRs after the pandemic, and the degree of improvement was related to the extent to which a location was affected. The findings also suggest that administrative services in the healthcare sector need further improvement. Based on these results, we summarize relevant recommendations at the end of this paper.


Subject(s)
COVID-19 , Physicians , Humans , Physician-Patient Relations , COVID-19/epidemiology , Pandemics , Data Mining/methods , China/epidemiology
16.
Int J Environ Res Public Health ; 19(20)2022 Oct 14.
Article in English | MEDLINE | ID: covidwho-2071450

ABSTRACT

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.


Subject(s)
COVID-19 , Social Media , Humans , COVID-19 Vaccines , Pandemics/prevention & control , COVID-19/epidemiology , COVID-19/prevention & control , Public Opinion , China/epidemiology
17.
Information Technology and Control ; 51(3):409-428, 2022.
Article in English | Scopus | ID: covidwho-2067092

ABSTRACT

First seen inWuhan, China, coronavirus (COVID-19) became a worldwide epidemic. Turkey's first reported case was announced on March 11, 2020—the day the World Health Organization declared COVID-19 is a pandemic. Due to the intense and widespread use of social media during the pandemic, determining social media's role and effect (i.e., positive, negative, neutral) gives us essential information about society's perspective on events. In our study, two datasets (i.e., Dataset1, Dataset2) consisting of Instagram comments on COVID-19 were com posed between different dates of the pandemic, and the change between users' feelings and thoughts about the epidemic was analyzed with Latent Dirichlet Allocation (LDA) and text mining algorithms. The datasets are the first publicly available Turkish datasets on the sentiment analysis of COVID-19, as far as we know. The sentiment analysis of Turkish Instagram comments was performed using machine learning models (i.e., traditional machine learning (TML), deep learning (DL), and Bidirectional Encoder Representations from Transformers (BERT)-based transfer learning). The balanced versions of these datasets (i.e., resDataset1, resDataset2) in the experiments were evaluated with the original ones. Compared with TML models (i.e., Support Vector Machine (SVM), Naïve Bayes (NB), Random Forest (RF)) and DL models (i.e., Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Convolutional Recurrent- Neural Networks (GCR-NN), the BERT-based transfer learning model achieved the highest classification success with 0.7864 macro-averaged F1-score values in resDataset1 and 0.7120 in resDataset2. It has been proven that using a pre-trained language model in Turkish datasets is more successful than other models in terms of classification performance. © Karayiğit, H., Akdagli, A., Acı, Ç. Í.

18.
EWHA Medical Journal ; 45(2):46-54, 2022.
Article in Korean | EMBASE | ID: covidwho-2067252

ABSTRACT

Objectives: Public health risks and anxiety have been increasing since the outbreak of Coronavirus disease 19 (COVID-19). The public expresses questions related to the COVID-19 issue through the web base. The aim of this study was to analyze public perception and sentiments of COVID-19 Pandemic in South Korea. Methods: We collected the text data (questions: 252, 181) related to COVID-19 from Naver Knowledge-iN during January 1, 2020 to December 31, 2020. The search keywords included related to COVID-19 using Korean words for “SARS-Cov-2”, “COVID19”, “COVID-19”, “Wuhan pneumonia”, “Coronavirus”, “Corona”. A topic modeling analysis was used to investigate and search trends of public perception. The sentiment analysis was conducted to analyze of public emotions in the questions related to COVID-19. We performed the Pearson's correlation analysis between daily number of COVID-19 cases and daily proportion of negative sentiment in documents related to COVID-19 by COVID-19 outbreak period. Results: A total of 241, 776 documents used in this study. The most frequent words in the documents to appear cough, symptoms, tests, confirmed patients, mask and etc. Twenty topics (COVID-test, Economy, School, Hospital/Diagnose, Travel/Overseas, Health, Social issue, Symptom 1 (respiratory), Relationships, Symptom 2 (e.g., fever), Workplace, Mask/Social distancing, infection/Vaccine, Stimulus Package, Family, Delivery Service, Unclassified, Region, Study/Exam, Worry, Anxiety) were extracted using the topic modeling. There was a positive association between the daily counts of COVID-19 patients and proportion of negative sentiment. By COVID-19 period, Stage 4 had the highest correlation. Conclusion: This study identified the South Korean public's interest and emotions about COVID-19 during the prolonged pandemic crisis.

19.
2nd International Conference on Computing and Machine Intelligence, ICMI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2063261

ABSTRACT

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.

20.
European Journal of Marketing ; 2022.
Article in English | Scopus | ID: covidwho-2063159

ABSTRACT

Purpose: This study aims to conceptualise the panic buying behaviour of consumers in the UK during the novel COVID-19 crisis, using the assemblage approach as it is non-deterministic and relational and affords new ways of understanding the phenomenon. Design/methodology/approach: The study undertakes a digital ethnography approach and content analysis of Twitter data. A total of 6,803 valid tweets were collected over the period when panic buying was at its peak at the beginning of the first lockdown in March 2020. Findings: The panic buying phase was a radical departure from the existing linguistic, discursive, symbolic and semiotic structures that define routine consumer behaviour. The authors suggest that the panic buying behaviour is best understood as a constant state of becoming, whereby stockpiling, food waste and a surge in cooking at home emerged as significant contributors to positive consumer sentiments. Research limitations/implications: The authors offer unique insights into the phenomenon of panic buying by considering DeLanda’s assemblage theory. This work will inform future research associated with new social meanings of products, particularly those that may have been (re)shaped during the COVID-19 crisis. Practical implications: The study offers insights for practitioners and retailers to lessen the intensity of consumers’ panic buying behaviour in anticipation of a crisis and for successful crisis management. Originality/value: Panic buying took on a somewhat carnivalesque hue as consumers transitioned to what we consider to be atypical modes of purchasing that remain under-theorised in marketing. Using the conceptual lenses of assemblage, the authors map bifurcations that the panic buyers’ assemblages articulated via material and immaterial bodies. © 2022, Emerald Publishing Limited.

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