Text Mining and Emotion Classification on Monkeypox Twitter Dataset: A Deep Learning-Natural Language Processing (NLP) Approach

Emotion classification has become a valuable tool in analyzing text and emotions people express in response to events or crises, particularly on social media and other online platforms. The recent news about monkeypox highlighted various emotions individuals felt during the outbreak. People’s opinions and concerns have been very different based on their awareness and understanding of the disease. Although there have been studies on monkeypox, emotion classification related to this virus has not been considered. As a result, this study aims to analyze the emotions individual expressed on social media posts related to the monkeypox disease. Our goal is to provide real-time information and identify critical concerns about the disease. To conduct our analysis, first, we extract and preprocess 800,000 datasets and then use NRCLexicon, a Python library, to predict and measure the emotional significance of each text. Secondly, we develop deep learning models based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and the combination of Convolutional Neural Networks and Long Short-Term Memory (CLSTM) for emotion classification. We use SMOTE (Synthetic Minority Oversampling Technique) and Random Undersampling techniques to address the class imbalance in our training dataset. The results of our study revealed that the CNN model achieved the highest performance with an accuracy of 96%. Overall, emotion classification on the monkeypox dataset can be a powerful tool for improving our understanding of the disease. The findings of this study will help develop effective interventions and improve public health. Author

Evaluation of Hotel Performance with Sentiment Analysis by Deep Learning Techniques

The subject of sentiment analysis through social media sites has witnessed significant development due to the increasing reliance of people on social media in advertising and marketing, especially after the Corona pandemic. There is no doubt that the prevalence of the Arabic language makes it considered one of the most important languages all over the world. Through human comments, it can know things if they are positive or negative. But in fact, the comments are many, and it takes work to evaluate the place or the product through a detailed reading of each comment. Therefore, this study applied deep learning approaches to this issue to provide final results that could be utilized to differentiate between the comments in the dataset. Arabic Sentiment Analysis was used and gave a percentage for each positive and negative commentary. This work used eight methods of deep learning techniques after using Fast Text as embedding, except Ara BERT. These techniques are the transformer (AraBERT), RNN (Long short-term memory (LSTM), Bidirectional long-short term memory (BILSTM), Gated recurrent units (GRUs), Bidirectional Gated recurrent units (BIGRU)), CNN (like ALEXNET, proposed CNN), and ensemble model (CNN with BI-GRU). The Hotel Arabic Reviews Dataset was utilized to test the models. This paper obtained the following results. In the Ara BERT model, the accuracy is 96.442%. In CNN, like the Alex Net model, the accuracy is 93.78%. In the suggested CNN model, the accuracy is 94.43%. In the suggested LSTM model, the accuracy is 95%. In the suggested BI-LSTM model, the accuracy is 95.11%. The accuracy of the suggested GRU model is 95.07%. The accuracy of the suggested BI-GRU model is 95.02%. The accuracy is 94.52% in the Ensemble CNN with BI-GRU model that has been proposed. Consequently, the AraBERT outperformed the other approaches in terms of accuracy. Because the AraBERT has already been trained on some Arabic Wikipedia entries. The LSTM, BI-LSTM, GRU, and BI-GRU, on the other hand, had comparable outcomes. © 2023, International Journal of Interactive Mobile Technologies. All Rights Reserved.

What does the media tell us about a destination? The Cancun case, seen from the USA, Canada, and Mexico

PurposeThis study aims to present a framework for automatically collecting, cleaning and analyzing text (news articles, in this case) to provide valuable decision-making information to destination management organizations. Keeping a record of certain aspects of the projected destination image of an attraction (Cancun in this study) will grant the design of better strategies for the promotion and administration of destinations without the time-consuming effort of manually evaluating high quantities of textual information. Design/methodology/approachUsing Web scraping, news articles were collected from the USA, Mexico and Canada over an interval of one year. The documents were analyzed using an automatic topic modeling method known as Latent Dirichlet Allocation and a coherence analysis to determine the number of themes present in each collection. With the data provided, the authors were able to extract valuable information to understand how Cancun is presented to the countries. FindingsIt was found that in all countries, Cancun is an important destination to travel and vacation;however, given the period defined for this study (from July 2021 to July 2022), an important part of the articles analyzed was concerned with the sanitary measures derived from the COVID-19 pandemic. Besides, given the rise of violence and the threat of organized crime, many articles from the three countries are focused on warning potential tourists about the risks of traveling to Cancun. Originality/valueThe examination of the relevant literature revealed that similar analyses are manually performed by the experts on a set of predefined categories. Although those approaches are methodologically sound, the logistic effort and the time used could become prohibitively expensive, precluding carrying out this analysis frequently. Additionally, the preestablished categories to be studied in press articles may distort the results. For these reasons, the proposed framework automatically allows for gathering valuable information for decision-making in an unbiased manner.

Panic buying and fake news in urban vs. rural England: A case study of twitter during COVID-19

This paper explores the potential association between the spread of fake news and the panic buying behavior, in urban and rural UK, widely accessible on Twitter since COVID 19 was announced by the WHO as a global pandemic. It describes how consumer's behavior is affected by the content generated over social media and discuss various means to control such occurrence that results in an undesirable social change. The research methodology is based on extracting data from texts on the subject of panic buying and analysing both the total volume and the rate of fake news classification during COVID-19, through crowdsourcing techniques with text-mining and Natural Language Processing models. In this paper, we have extracted the main topics in different phases of the pandemic using term frequency strategies and word clouds as well as applied artificial intelligence in exploring the reliability behind online written text on Twitter. The findings of the research indicate an association between the pattern of panic buying behavior and the spread of fake news among urban and rural UK. We have highlighted the magnitude of the undesired behavior of panic buying and the spread of fake news in the rural UK in comparison with the urban UK.

Sentiment Analysis on COVID-19 Vaccination in Bangladesh

The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories: positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%. © 2022 IEEE.

A Deep Learning Approach for Sentiment Classification of COVID-19 Vaccination Tweets

Now-a-days, social media platforms enable people to continuously express their opinions and thoughts about different topics. Monitoring and analyzing the sentiments of people is essential for governments and business organizations to better understand people's feelings and thoughts. The Coronavirus disease 2019 (COVID-19) has been one of the most trending topics on social media over the last two years. Consequently, one of the preventative measures to control and prevent the spread of the virus was vaccination. A dataset was formed by collecting tweets from Twitter for over a month from November 13th to December 31st, 2021. After data cleaning, the tweets were assigned a positive, negative, or neutral label using a natural language processing (NLP) sentiment analysis tool. This study aims to analyze people's public opinion towards the vaccination process against COVID-19. To fulfil this goal, an ensemble model based on deep learning (LSTM-2BiGRU) is proposed that combines long short-term memory (LSTM) and bidirectional gated recurrent unit (BiGRU). The performance of the proposed model is compared to five traditional machine learning models, two deep learning models in addition to state-of-the-art models. By comparing the results of the models used in this study, the results reveal that the proposed model outperforms all the machine and deep learning models employed in this work with a 92.46% accuracy score. This study also shows that the number of tweets that involve neutral, positive, and negative sentiments is 517496 (37%) tweets, 484258 (34%) tweets, and 409570 (29%) tweets, respectively. The findings indicate that the number of people carrying neutral sentiments towards COVID-19 immunization through vaccines is the highest among others. © 2023, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Tweets and PCR Test-based Analysis and Prediction of Social Response to a Future Pandemic. A Case Study

The COVID-19 pandemic has greatly affected our society badly. It has been a subject of discussion since 2019 due to the increased prevalence of social media and its extensive use, and it has been a source of tension, fear, and disappointment for people all over the world. In this research, we took data from COVID-19 tweets from 10 different regions from July 25, 2020, to August 29, 2020. Using the well-known word embedding technique count-vectorizer, we experimented with different machine learning classifiers on data to train deep neural networks to improve the accuracy of predicted opinions with a low elapsed time. In addition, we collected PCR results from these regions for the same time interval. We compared the opinions in the form of positive or negative responses with the results of the PCR tests per million people. With the help of the results, We figured out a real-time international measure to detect these regions' behaviors for any future pandemic. If we know how a region thinks about an upcoming pandemic, then we can predict the region's real-time behavior for the particular pandemic. This would happen if we had past case studies to compare, like in our proposed research. Copyrights © 2023 The Institute of Electronics and Information Engineers.

Digital topics on cultural heritage investigated: how can data-driven and data-guided methods support to identify current topics and trends in digital heritage?

In research and policies, the identification of trends as well as emerging topics and topics in decline is an important source of information for both academic and innovation management. Since at present policy analysis mostly employs qualitative research methods, the following article presents and assesses different approaches – trend analysis based on questionnaires, quantitative bibliometric surveys, the use of computer-linguistic approaches and machine learning and qualitative investigations. Against this backdrop, this article examines digital applications in cultural heritage and, in particular, built heritage via various investigative frameworks to identify topics of relevance and trendlines, mainly for European Union (EU)-based research and policies. Furthermore, this article exemplifies and assesses the specific opportunities and limitations of the different methodical approaches against the backdrop of data-driven vs. data-guided analytical frameworks. As its major findings, our study shows that both research and policies related to digital applications for cultural heritage are mainly driven by the availability of new technologies. Since policies focus on meta-topics such as digitisation, openness or automation, the research descriptors are more granular. In general, data-driven approaches are promising for identifying topics and trendlines and even predicting the development of near future trends. Conversely, qualitative approaches are able to answer "why” questions with regard to whether topics are emerging due to disruptive innovations or due to new terminologies or whether topics are becoming obsolete because they are common knowledge, as is the case for the term "internet”.

Time Series Analysis of Sentiment: A Comparison of the US and UK Coronavirus Subreddits

In this paper, we investigate the dynamics of the social media response on Reddit to the COVID-19 pandemic during its first year (February 2020-2021). The emergence of region-specific subreddits allows us to compare the reactions of individuals posting their opinions on social media about the global pandemic from two perspectives - the UK and the US.In particular, we look at the volume of posts and comments on these two subreddits, and at the sentiment expressed in these posts and comments over time as a measure of the public level of engagement and response. Whilst an analysis of volume allows us to quantify how interested people are about the pandemic as it unfolds, sentiment analysis goes beyond this and informs us about how people respond towards the pandemic based on the textual content in the posts and comments. The research looks to develop a framework for analyzing the social response on Reddit to a large-scale event in terms of the level of engagement measured through post and comment volumes, and opinion measured through an analysis of sentiment applied to the post content. In order to compare the subreddits, we show the trend in the time series through the application of moving average methods. We also show how to identify the lag between time series and align them using cross-correlation. Moreover, once aligned, we apply moving correlations to the time series to measure their degree of correspondence to see if there is a similar response to the pandemic across the two groups (UK and US). The results indicate that both subreddits were posting in high volumes at specific points during the pandemic, and that, despite the generally negative sentiment in the posts and comments, a gradual decrease in negativity leading up to the start of 2021 is observed as measures are put in place by governments and organizations to contain the virus and provide necessary support the affected populations.

A Multimodal Approach for Event Detection: Study of Uk Lockdowns in the Year 2020

Satellites allow spatially precise monitoring of the Earth, but provide only limited information on events of societal impact. Subjective societal impact, however, may be quantified at a high frequency by monitoring social media data. In this work, we propose a multi-modal data fusion framework to accurately identify periods of COVID-19-related lockdown in the United Kingdom using satellite observations (NO2 measurements from Sentinel-5P) and social media (textual content of tweets from Twitter) data. We show that the data fusion of the two modalities improves the event detection accuracy on a national level and for large cities such as London.

The impact of the learning shift during COVID-19 on students using natural language processing

On 9th March 2020, Saudi Arabia has proclaimed the temporary transition to remote learning due to COVID-19. We underline students' perspectives on this abrupt transformation. We generate a word cloud based on the students' responses concerning the rapid transition. The feedback based on emotions was classified and a word cloud for each emotion was generated. For better decision making and improved strategies, we highlight the major problems and benefits of remote learning and provide some recommendations. Students have experienced a variety of hurdles, including the lack of an adequate study environment and technical difficulties, particularly when taking exams. Many were under psychological pressure. Others saw an increase in cheating. Some struggled to work with their peers on group projects, some sought tutoring, and others faced financial difficulties. Online practical sessions were found to be unsuitable for some disciplines. The flexibility of learning and saving money and time were the main advantages of remote learning.

E-learning university evaluation through sentiment analysis centered on user experience dimensions

center dot The COVID-19 crisis increased the number of users of university online teaching, enhancing the importance of this learning format. Additionally, ISO 9241-210:2019 standard sets the international standards for the design of products, services and interaction systems from usability, accessibility, and user experience (User eXperience -UX) perspective. Then, in order to design interfaces and learning experiences that include motivations, feelings and needs of end users, it is necessary to previously evaluate the UX of these environments, with less general and/or laborious methods than those that currently exist. Therefore, this work aims to establish the basis of a method that allows automatically to evaluate the UX of online teaching platforms by analyzing the users' sentiment about specific aspects of their virtual learning experience. To do this, 2,035 users were surveyed about their online learning experience with a questionnaire and an open text field to give their opinion. The population surveyed were online postgraduate students of the Universitat de Valencia and the Universidad Rey Juan Carlos, and university students of massive open online courses of the Universitat Politecnica de Valencia. The opinions collected in Spanish from 476 students were processed with the commercial sentiment analysis and natural language processing tool MeaningCloud, to analyze the sentiment (positive, negative, or neutral) about aspects of their experience. The results present a new model that, on the one hand, ontologically classifies categories and aspects of online education with sentiment analysis techniques, and on the other hand, the model groups these categories according to UX criteria, presenting its own classification to facilitate the evaluation of online learning experiences in a concrete and automatic way.

The impact of overtourism on residential well-being - The case of Budapest (Hungary) District VII

Overtourism has a number of negative impacts on both the attractiveness of tourist destinations and the life of local residents. The period of tourism that converged to almost nothing due to the outbreak and global spread of the COVID-19 pandemic is a suitable time for examining residents' perceptions of (over)tourism. In this study, the research question focuses on the impact of overtourism on the residential well-being of local communities. Answering the questions that arise from the theoretical positioning, involving developing knowledge of the related impacts, is important because such new patterns of behavior may become commonplace. Sentiment analysis was chosen to answer the research questions and proved to be a good tool for exploring the impacts of overtourism perceived by local residents in an unconventional way. Through sentiment analysis based on neuro-linguistic programming (NLP) methodology, three key aspects of human experience - neurology, language, and programming - became the focus of investigation.The results, based on 13,145 comments show which sensory perceptions transmitted by representational systems - such as sight, hearing, touch, taste, and smell - were more significant in the case of the examined keywords. For all keywords, regardless of the annual distribution, employment of the visual representation system (which represents visual modalities) was prominent, followed by the olfactory representation system representing olfactory modalities. The use of auditory-related acoustic, perceptual kinesthetic, and taste-like gustatory representation systems appears to have been less important and nearly equal in terms of the records that were examined. By understanding the correlation between overtourism and residential well-being, non-governmental-organizations and local municipal governments - which provides housing for residents - can more effectively shape the factors that influence residential well-being, while local residents who are exposed to the environmental impacts of tourism can also play an important role in shaping their own residential well-being.

Dynamics of social media behavior before and after SARS-CoV-2 infection.

Introduction: Online social media have been both a field of research and a source of data for research since the beginning of the COVID-19 pandemic. In this study, we aimed to determine how and whether the content of tweets by Twitter users reporting SARS-CoV-2 infections changed over time. Methods: We built a regular expression to detect users reporting being infected, and we applied several Natural Language Processing methods to assess the emotions, topics, and self-reports of symptoms present in the timelines of the users. Results: Twelve thousand one hundred and twenty-one twitter users matched the regular expression and were considered in the study. We found that the proportions of health-related, symptom-containing, and emotionally non-neutral tweets increased after users had reported their SARS-CoV-2 infection on Twitter. Our results also show that the number of weeks accounting for the increased proportion of symptoms was consistent with the duration of the symptoms in clinically confirmed COVID-19 cases. Furthermore, we observed a high temporal correlation between self-reports of SARS-CoV-2 infection and officially reported cases of the disease in the largest English-speaking countries. Discussion: This study confirms that automated methods can be used to find digital users publicly sharing information about their health status on social media, and that the associated data analysis may supplement clinical assessments made in the early phases of the spread of emerging diseases. Such automated methods may prove particularly useful for newly emerging health conditions that are not rapidly captured in the traditional health systems, such as the long term sequalae of SARS-CoV-2 infections.

Covid-19 Pandemic Predictive System Using Machine Learning

Corona -virus disease commonly known as COVID-19 that outbreak in late December 2019 is continuously spreading worldwide and infecting people due to which it's required to analysis research on the expansion of CODID-19.In this research, a more improved model. HYBRID ARTIFICAL MODEL (AI) is suggested for prediction. In conventional model, it treats similar infection rate for all people, an improvised ISI (improved susceptible-infected) is suggested to gauge the infection rate to calculate the development mode. We have build the hybrid AI model by using natural language processing(NLP) model and long short-term memory(LSTM) network modules inside ISI module.According to the attentive results, it represents more infections from three to eight days.In comparison to both the models , our developed new AI model can remarkably reduces the prediction result's error and prevail the mean percentage errors with different percentage for the six consecutive days in different countries.For example-China , Italy, France, etc. © 2022 IEEE.

Into the Unknown: Uncertainty, Foreboding and Financial Markets

While the link between financial market movement and economic policy uncertainty indices is well-established in literature, uncertainty in the form of ‘foreboding' emanating from catastrophic events has not been explored in literature. This paper explores "foreboding”, which reflects uncertainty at its extreme, following the Covid-19 pandemic. Using Natural Language Processing on minute-by-minute news data, I construct two Foreboding Indices, representing ‘foreboding' or ‘fearful apprehension', for 28,622 Covid-related news for the period July 2020–August 2021. The impact of foreboding on financial market volatility is explored using a logistic regression model. Both the indices show a marked increase in June–July, 2020, in January 2021, April, 2021, and July–August, 2021 and have a positive impact on volatility for hourly S&P 500 Index. Understanding of foreboding sentiment is crucial for central banks looking to monitor financial market volatility. Appropriate signaling in accordance to sentiment can help central banks handle detrimental impacts of market volatility. Moreover, FI can be used for market practitioners to gauge the sentiment and take effective trading decisions. © 2023, The Author(s), under exclusive licence to Springer Japan KK, part of Springer Nature.

Assessing the Impact of Contextual Information in Hate Speech Detection

Social networks and other digital media deal with huge amounts of user-generated contents where hate speech has become a problematic more and more relevant. A great effort has been made to develop automatic tools for its analysis and moderation, at least in its most threatening forms, such as in violent acts against people and groups protected by law. One limitation of current approaches to automatic hate speech detection is the lack of context. The spotlight on isolated messages, without considering any type of conversational context or even the topic being discussed, severely restricts the available information to determine whether a post on a social network should be tagged as hateful or not. In this work, we assess the impact of adding contextual information to the hate speech detection task. We specifically study a subdomain of Twitter data consisting of replies to digital newspapers posts, which provides a natural environment for contextualized hate speech detection. We built a new corpus in Spanish (Rioplatense variant) focused on hate speech associated to the COVID-19 pandemic, annotated using guidelines carefully designed by our interdisciplinary team. Our classification experiments using state-of-the-art transformer-based machine learning techniques show evidence that adding contextual information improves the performance of hate speech detection for two proposed tasks: binary and multi-label prediction, increasing their Macro F1 by 4.2 and 5.5 points, respectively. These results highlight the importance of using contextual information in hate speech detection. Our code, models, and corpus has been made available for further research. © 2013 IEEE.

Feedback Analysis of Online Teaching Using SVM

Study examined how we can upgrade the quality of online teaching (Feng and Bienkowski in Enhancing teaching and learning through educational data mining and learning analytics. Department of Education, Office of Educational Technology [Feng M, Bienkowski M (2012) Enhancing teaching and learning through educational data mining and learning analytics. Department of Education, Office of Educational Technology]) in the upcoming years. Data were collected through the Google form which were filled up by the students. Feedback is the most important attribute of assessment as it provides students with a statement of their learning and advises how to improve. Result was helping to enhance the quality of online teaching in educational system and also provide the constituent which helps in improving the online teaching. We will use sentiment analysis (Kumar and Nezhurina in Sentiment analysis on tweets for trains using machine learning. Research Gate, p 10 [Kumar S, Nezhurina MI (2020) Sentiment analysis on tweets for trains using machine learning. Research Gate, p 10]) also. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detect misinformation of COVID-19 using deep learning: A comparative study based on word embedding

Since its emergence in December 2019, there have been numerous news of COVID-19 pandemic shared on social media, which contain information from both reliable and unreliable medical sources. News and misleading information spread quickly on social media, which can lead to anxiety, unwanted exposure to medical remedies, etc. Rapid detection of fake news can reduce their spread. In this paper, we aim to create an intelligent system to detect misleading information about COVID-19 using deep learning techniques based on LSTM and BLSTM architectures. Data used to construct the DL models are text type and need to be transformed to numbers. We test, in this paper the efficiency of three vectorization techniques: Bag of words, Word2Vec and Bert. The experimental study showed that the best performance was given by LSTM model with BERT by achieving an accuracy of 91% of the test set. © 2023 IEEE.

Systematic Assessment and Illustration of Students' Online Discussion Engagement

During Covid-19, most of the university courses have been transited to distance education and online teaching. It is unfortunate but true that most of this distance teaching lacks the active participation of learners, thus failing to keep their attention throughout the teaching time. According to "Dale's Cone of Learning,” students only remember 20 percent of the lecture without the involvement of any active participation. So it has become critical to find ways to engage these distance learners actively. The online discussion forum can be utilized as the primary tool to intrigue the active engagement of the learners. Therefore, it is also necessary to measure the students' active engagement in online course discussion forums. Digitalization of the teaching process allows access to a large amount of data representing learners' behavior. Every click can be observed and analyzed, which allows automating the assessment of the learning process. This paper aims to employ an existing approach introduced by the authors in previous work to translate learner's engagement quantitatively based on their online discussion activity, which can be further utilized for assessment and understanding the interaction dynamics within the course. The discussion forum data from three cohorts of an online course on "Systematic Creativity and TRIZ basics” at LUT University, Finland, is analyzed via employing Social Network Analysis and natural language processing (NLP). Learners' engagement in the discussion forums is assessed by focusing on two main criteria: the number of meaningful words used and the centrality measure of network analysis. As a result, the assessment of 50+ students' discussion forum activity depicts a strong correlation between the meaningful words used by the students and their interaction (degree centrality and eigenvector centrality). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.