Modelling Predictability of Airbnb Rental Prices in Post COVID-19 Regime: An Integrated Framework of Transfer Learning, PSO-Based Ensemble Machine Learning and Explainable AI

In this research, an effort has been put to develop an integrated predictive modeling framework to automatically estimate the rental price of Airbnb units based on listed descriptions and several accommodation-related utilities. This paper considers approximately 0.2 million listings of Airbnb units across seven European cities, Amsterdam, Barcelona, Brussels, Geneva, Istanbul, London, and Milan, after the COVID-19 pandemic for predictive analysis. RoBERTa, a transfer learning framework in conjunction with K-means-based unsupervised text clustering, was used to form a homogeneous grouping of Airbnb units across the cities. Subsequently, particle swarm optimization (PSO) driven advanced ensemble machine learning frameworks have been utilized for predicting rental prices across the formed clusters of respective cities using 32 offer-related features. Additionally, explainable artificial intelligence (AI), an emerging field of AI, has been utilized to interpret the high-end predictive modeling to infer deeper insights into the nature and direction of influence of explanatory features on rental prices at respective locations. The rental prices of Airbnb units in Geneva and Brussels have appeared to be highly predictable, while the units in London and Milan have been found to be less predictable. Different types of amenity offerings largely explain the variation in rental prices across the cities.

ADAPTATION OF DOMAIN-SPECIFIC TRANSFORMER MODELS WITH TEXT OVERSAMPLING FOR SENTIMENT ANALYSIS OF SOCIAL MEDIA POSTS ON COVID-19 VACCINE

Covid-19 has spread across the world, and several vaccines have been developed to counter its surge. To identify the correct sentiments that are associated with the vaccines from social media posts, we fine-tune various state-of-the-art pretrained transformer models on tweets that are associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art RoBERTa, XLNet, and BERT pre-trained transformer models, and the domain-specific CT-BERT and BERTweet transformer models that have been pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using the language model-based oversampling technique (LMOTE) to improve the accuracies of these models – specifically, for small sample data sets where there is an imbalanced class distribution among the positive, negative, and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced small-sample data sets that are used to fine-tune state-of-the-art pre-trained transformer models as well as the utility of domain-specific transformer models for the classification task. © 2023 Author(s). This is an open access publication, which can be used, distributed and reproduced in any medium according to the Creative Commons CC-BY 4.0 License.

COVID-19 Fake News Detection using Pre-Tuned BERT-Based Transfer Learning Models

The World Health Organization declared the Coronavirus disease in 2019;that was a very hard time for people, and every single day, a new crisis emerged. In that case, everyone shares their stories on the social media platform on a daily basis, but no one is sure that information is true or misleading, and that becomes a challenge to detect differences between them. And to tackle this problem, this paper has explored the veracity of social media stories using some machine learning models. The goal of this paper is to test three different BERT base pre-trained transform learning models (BERT, DistilBERT, and RoBERTa) on an English COVID-19 fake news dataset to detect the fake and true news separately. We explore their capability with precision, recall, and F1-score to achieve better results compared with the previous research. © 2022 IEEE.

Analyzing the Performance of Sentiment Analysis using BERT, DistilBERT, and RoBERTa

Sentiment analysis or opinion mining is a natural language processing (NLP) technique to identify, extract, and quantify the emotional tone behind a body of text. It helps to capture public opinion and user interests on various topics based on comments on social events, product reviews, film reviews, etc. Linear Regression, Support Vector Machines, Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM (Long Short Term Memory), and other machine learning and deep learning algorithms can be used to analyze the sentiment behind a text. This work analyses the sentiments behind movie reviews and tweets using the Coronavirus tweets NLP dataset and Sentiment140 dataset. Three advanced transformer-based deep learning models like BERT, DistilBERT, and RoBERTa are experimented with to perform the sentiment analysis. Finally, the performance obtained using these models on these two different datasets is compared using the accuracy as the performance evaluation matrix. On analyzing the performance, it can be seen that the BERT model outperforms the other two models. © 2022 IEEE.

Predicting COVID-19 Related Tweets Using Ensemble of Transformers Models

In the past few years, COVID-19 has been consid-ered one of the most dangerous pandemics in several countries. There is a lot of information circulating on social media platforms about COVID-19, some of it is reliable, while others may be exag-gerated or unfounded. Using machine learning-driven sentiment analysis is considered a valuable tool that helps understand the community's feelings regarding many issues like the COVID-19 outbreak. Developing an accurate model that can assess if a tweet is about COVID-19 is a challenging task. This study aims to classify the tweets whether it is about COVID-19 or not using deep learning and transformers models. The developed model improves the gathering of tweets data about the COVID-19 epidemic without relying only on keywords such as 'covid' or 'coronavirus'. In this work, we proposed the best model based on an ensemble method that effectively combines three models which are: BERTweet-covid19-base-cased, BERTweet, and RoBERTa. We applied the models to the data set provided by the Zindi community. The best results were achieved over the tested dataset in terms of Log-Loss with a minimum value of 0.154,0.174,0.170, and 0.191 for the proposed ensemble model, BERTweet-covid19-base-cased, BERTweet, and RoBERTa respectively. Our proposed model is ranked first among all the participant teams. © 2022 IEEE.

Construction and Analysis of Surrounding Travel Demanding Graph Based on Dual Contrastive Learning Text Classification and Graph Neural Network

Understanding the main information about the current situation of the tourism market has become an urgent need and new trends in the development of the tourism market. In this paper, we use natural language processing technology to analyze the development of tourism around Maoming City, Guangdong Province during the COVID-19 epidemic by means of data mining methods to build a local tourism graph, refine and design models and methods such as RoBERTa-BiGRU-Attention fusion model, dual contrastive learning, BERT-BiLSTM-CRF named entity identification technique, improved Apriori algorithm, GNNLP model based on conventional models and proved the rationality and efficiency of the improved model by comparative test, provide oriented suggestions to help government departments promote tourism and tourism enterprises product supply, optimize resource allocation and explore the market constantly during the epidemic period after scientific analysis and summary. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Modelling Predictability of Airbnb Rental Prices in Post COVID-19 Regime: An Integrated Framework of Transfer Learning, PSO-Based Ensemble Machine Learning and Explainable AI

In this research, an effort has been put to develop an integrated predictive modeling framework to automatically estimate the rental price of Airbnb units based on listed descriptions and several accommodation-related utilities. This paper considers approximately 0.2 million listings of Airbnb units across seven European cities, Amsterdam, Barcelona, Brussels, Geneva, Istanbul, London, and Milan, after the COVID-19 pandemic for predictive analysis. RoBERTa, a transfer learning framework in conjunction with K-means-based unsupervised text clustering, was used to form a homogeneous grouping of Airbnb units across the cities. Subsequently, particle swarm optimization (PSO) driven advanced ensemble machine learning frameworks have been utilized for predicting rental prices across the formed clusters of respective cities using 32 offer-related features. Additionally, explainable artificial intelligence (AI), an emerging field of AI, has been utilized to interpret the high-end predictive modeling to infer deeper insights into the nature and direction of influence of explanatory features on rental prices at respective locations. The rental prices of Airbnb units in Geneva and Brussels have appeared to be highly predictable, while the units in London and Milan have been found to be less predictable. Different types of amenity offerings largely explain the variation in rental prices across the cities.

A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models

The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. Amid the COVID-19 epidemic, this misleading information has aggravated the situation by putting people's mental and physical lives in danger. To limit the spread of such inaccuracies, identifying the fake news from online platforms could be the first and foremost step. In this research, the authors have conducted a comparative analysis by implementing five transformer-based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID-19 from the internet. COVID-19 Fake News Dataset has been used for training and testing the models. Among all these models, the RoBERTa model has performed better than other models by obtaining an F1 score of 0.98 in both real and fake classes. © 2022 IEEE.

iCompass at CheckThat! 2022: Combining Deep Language Models for Fake News Detection

Users of social media tend to explore different platforms to obtain news and find information about different events and activities. Furthermore they read, share, publish news with no prior knowledge of the certainty of being real or fake. This necessitates the development of an automated system for fake news detection. In this paper we report a system and its output as part of CLEF2022 - CheckThat! Lab Fighting the COVID-19 Infodemic and Fake News Detection. Task 3 was carried out using two BERT base uncased and data preprocessing with stop-words removal, lemmatization. We achieve an F1 score of 0.339 on news classification on English dataset. © 2022 Copyright for this paper by its authors.

Sentiment Analysis of COVID-19 Vaccination Responses From Twitter Using Ensemble Learning

Despite the evidence that shows the benefits and safety of immunizations, the widespread vaccine-related misinformation and conspiracy theories online have fueled a general vaccine hesitancy, and coronavirus disease (COVID-19) vaccinations are no exception. COVID-19 vaccine hesitancy is considered a global threat to public health that undermines the efforts to control the COVID-19 pandemic. Twitter and other social media platforms allow people to exchange information and express concerns and emotions on COVID-19-related issues. This research aims to understand people's sentiment on COVID-19 vaccines from data collected from Twitter. Analyzing the public attitude toward the vaccines helps the authorities to make better decisions and reach the intended herd immunity. In this paper, we utilize the state-of-the-art transformer-based classification models, RoBERTa and BERT, along with multiple task-specific versions, to classify people's opinions about COVID-19 vaccinations into positive, negative, and neutral. A Twitter dataset that consists of people's opinions about vaccines is used to train and evaluate the presented models. Two ensemble learning techniques that aggregate the individual classifiers are presented for further performance improvement: majority voting and stacking with Support Vector Machine (SVM) as meta-learner. The results also show that applying ensemble learning significantly outperforms the individual classifiers using all evaluation measures. We also found that ensembling with stacking has an advantage over simple majority voting. © 2022 IEEE.

COVID-19 Fake News Detection by Using BERT and RoBERTa models

We live in a world where COVID-19 news is an everyday occurrence with which we interact. We are receiving that information, either consciously or unconsciously, without fact-checking it. In this regard, it has become an enormous challenge to keep only true COVID-19 news relevant. People are exposed to these stories on a daily basis, and not all of them are true and fact-checked reports on the COVID-19 pandemic, which was the primary reason for our research. We accepted the challenge that fake news is extremely common and that some people take these news as they are. Knowing the true power of the most recent NLP achievements, in this research we focus on detecting fake news regarding COVID-19. Our approach includes using pre-trained BERT and RoBERTa models, which we then fine-tune on real and fake news about the COVID-19 pandemic. By using pre-trained BERT and RoBERTa models on tweet data, we explore their capabilities and compare them to previous research in regard to fine-tuned BERT models for this task in which we achieve better accuracy, recall and f1 score. © 2022 Croatian Society MIPRO.

Pre-trained ensemble model for identification of emotion during COVID-19 based on emergency response support system dataset.

The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition. This study examines the contents of calls landed in the emergency response support system (ERSS) during the pandemic. Furthermore, a combined analysis of Twitter patterns connected to emergency services could be valuable in assisting people in this pandemic crisis and understanding and supporting people's emotions. The proposed Average Voting Ensemble Deep Learning model (AVEDL Model) is based on the Average Voting technique. The AVEDL Model is utilized to classify emotion based on COVID-19 associated emergency response support system calls (transcribed) along with tweets. Pre-trained transformer-based models BERT, DistilBERT, and RoBERTa are combined to build the AVEDL Model, which achieves the best results. The AVEDL Model is trained and tested for emotion detection using the COVID-19 labeled tweets and call content of the emergency response support system. This is the first deep learning ensemble model using COVID-19 emotion analysis to the best of our knowledge. The AVEDL Model outperforms standard deep learning and machine learning models by attaining an accuracy of 86.46 percent and Macro-average F1-score of 85.20 percent.

Sentimental Analysis for Studying and Analyzing the Spreading of COVID-19 from Twitter Data

Coronavirus, known as COVID-19, rapidly spread on a wide scale in a short time consequently. World Health Organization (WHO) classified it as a global pandemic. Social networks news becomes a valuable resource for massive amounts of data and news about the epidemic in which news is deliberating every day. Twitter is one of these networks which is a popular platform that contains rich information and currently it repre-sents a rich resource of data about COVID-19. In this research, we study and analyze the spreading of the COVID-19 epidemic based on the location and dates using datasets from Twitter. Moreover, the study has done by performing sentiment analysis and making a correlation study between confirmed cases in a set of countries and the sentiment's polarity value including negative and positive as well as a correlation between the number of confirmed cases and number of tweets per country. Also, we have experimented with several machine learning classifiers including Naive base, Support Vector Machine, and Logistic Regression as well as RoBERTa model to predict the sentiment analysis on the dataset. The experimental results show that Logistic Regression outperforms other classifiers with an accuracy of 0.86%, thus, machine learning techniques could be used to study the sentiment of tweets which gives reasonable results. © 2021 IEEE.

Global User-Level Perception of COVID-19 Contact Tracing Applications: Data-Driven Approach Using Natural Language Processing.

BACKGROUND: Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. To this aim, several mobile apps have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing information from different sources, such as news and users' reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users' reviews and extracts the evoked sentiments. We believe such solutions combined with a user-friendly interface can be used as a rapid surveillance tool to monitor how effective an application is and to make immediate changes without going through an intense participatory design method. OBJECTIVE: In this paper, we aim to analyze the efficacy of AI and NLP techniques for automatically extracting and classifying the polarity of users' sentiments by proposing a sentiment analysis framework to automatically analyze users' reviews on COVID-19 contact tracing mobile apps. We also aim to provide a large-scale annotated benchmark data set to facilitate future research in the domain. As a proof of concept, we also developed a web application based on the proposed solutions, which is expected to help the community quickly analyze the potential of an application in the domain. METHODS: We propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding with the development and training of artificial intelligence (AI) models for automatic sentiment analysis of users' reviews. In detail, we collected and annotated a large-scale data set of user reviews on COVID-19 contact tracing applications. We used both classical and deep learning methods for classification experiments. RESULTS: We used 8 different methods on 3 different tasks, achieving up to an average F1 score of 94.8%, indicating the feasibility of the proposed solution. The crowd-sourcing activity resulted in a large-scale benchmark data set composed of 34,534 manually annotated reviews. CONCLUSIONS: The existing literature mostly relies on the manual or exploratory analysis of users' reviews on applications, which is tedious and time-consuming. In existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and natural language processing techniques provide good results for analyzing and classifying users' sentiments' polarity and that automatic sentiment analysis can help to analyze users' responses more accurately and quickly. We also provided a large-scale benchmark data set. We believe the presented analysis, data set, and proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile apps deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method.

Investigating the Emotional Response to COVID-19 News on Twitter: A Topic Modelling and Emotion Classification Approach

Media has played an important role in public information on COVID-19. But distressing news, e.g., COVID-19 death tolls, may trigger negative emotions in public, discouraging them from following the news, which, in turn, can limit the effectiveness of the media. To understand people’s emotional response to the COVID-19 news, we have investigated the prevalence of basic human emotions in around 19 million user responses to 1.7 million COVID-19 news posts on Twitter from (English-speaking) media across 12 countries from January 2020 to April 2021. We have used Latent Dirichlet Allocation (LDA) to identify news themes on Twitter. Also, the Robustly Optimized BERT Pretraining Approach (RoBERTa) model was used to identify emotions in the tweets. Our analysis of the Twitter data revealed that anger was the most prevalent emotion in user responses to the news coverage of COVID-19. That was followed by sadness, optimism, and joy, steadily over the period of the study. The prevalence of anger (in user responses) was higher for the news about authorities and politics while optimism and joy were more prevalent for the news about vaccination and educational impacts of COVID-19 respectively. The prevalence of sadness in user responses, however, was the highest for the news about COVID-19 cases and deaths and the impacts on the families, mental health, jails, and nursing homes.We also observed a higher level of anger in the user responses to the (COVID-19) news posted by the USA media accounts (e.g., CNN Politics, Fox News, MSNBC). Optimism, on the other hand, was found to be the highest for Filipino media accounts. Author

Self-Attention Mechanism of RoBERTa to Improve QAS for e-health Education

This paper aims to discuss about the establishment of a health education system in the form of a Question Answer System (QAS) related to the current COVID-19 pandemic. QAS allows users to state information needs in the form of natural language questions, and then this system will return short text quotes or sentence phrases as answers. This is due to the tendency for recipients of information to more easily understand news/information when they can answer questions that may arise in their minds. The approach used was self-attention mechanism such as a Robustly Optimized BERT Pretraining Approach (RoBERTa), a method for question answering with span-based training that predicting the starting limit for answer start and the end limit for the answer index. The final results using 835 non-description questions, the best evaluation value on the training data showed the exact match of 91.7% and F1 value of 93.3%. RoBERTa tends to show the better results on non- description questions or questions with short answers compared to the description questions with complex answers. © 2021 IEEE.

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study.

The pandemic COVID 19 has altered individuals' daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as 'lockdown,' 'emergency,' or curfew' to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member's behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February-June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country's citizens when facing future unexpected exogenous shocks.

COVID-19 outbreak: An ensemble pre-trained deep learning model for detecting informative tweets.

On 11 March 2020, the (WHO) World Health Organization declared COVID-19 (CoronaVirus Disease 2019) as a pandemic. A further crisis has manifested mass fear and panic, driven by lack of information, or sometimes outright misinformation, alongside the coronavirus pandemic. Twitter is one of the prominent and trusted social media in this current outbreak. Over time, boundless COVID-19 headlines and vast awareness have been spreading, with tweets, updates, videos, and explosive posts. Few studies have been performed on the pandemic to detect and interrelate various disease types, including current coronavirus. However, it is pretty tricky to discriminate and detect a specific category. This work is motivated by the need to inform society about limiting irrelevant information and avoiding spreading negative emotions. In this context, the current work focuses on informative tweet detection in the pandemic to provide relevant information to the government, medical organizations, victims services, etc. This paper used a Majority Voting technique-based Ensemble Deep Learning (MVEDL) model. This MVEDL model is used to identify COVID-19 related (INFORMATIVE) tweets. The state-of-art deep learning models RoBERTa, BERTweet, and CT-BERT are used for best performance with the MVEDL model. The "COVID-19 English labeled tweets" dataset is used for training and testing the MVEDL model. The MVEDL model has shown 91.75 percent accuracy, 91.14 percent F1-score and outperforms the traditional machine learning and deep learning models. We also investigate how to use the MVEDL model for sentiment analysis on 226668 unlabeled COVID-19 tweets and their informative tweets. The application section discussed a comprehensive analysis of both actual and informative tweets. According to our knowledge, this is the first work on COVID-19 sentiment analysis using a deep learning ensemble model.

Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach.

Newspapers are very important for a society as they inform citizens about the events around them and how they can impact their life. Their importance becomes more crucial and indispensable in the times of health crisis such as the current COVID-19 pandemic. Since the starting of this pandemic newspapers are providing rich information to the public about various issues such as the discovery of a new strain of coronavirus, lockdown and other restrictions, government policies, and information related to the vaccine development for the same. In this scenario, analysis of emergent and widely reported topics/themes/issues and associated sentiments from various countries can help us better understand the COVID-19 pandemic. In our research, the database of more than 100,000 COVID-19 news headlines and articles were analyzed using top2vec (for topic modeling) and RoBERTa (for sentiment classification and analysis). Our topic modeling results highlighted that education, economy, US, and sports are some of the most common and widely reported themes across UK, India, Japan, South Korea. Further, our sentiment classification model achieved 90% validation accuracy and the analysis showed that the worst affected country, i.e. the UK (in our dataset) also has the highest percentage of negative sentiment.