MLCNN-COV: A multilabel convolutional neural network-based framework to identify negative COVID medicine responses from the chemical three-dimensional conformer

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer-learning methodology and a multilabel convolutional neural network for COVID (MLCNN-COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer-learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN-COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three-dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN-COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer-learning model's performance. It shows that three-dimensional conformers adequately identify negative COVID medicine responses. 1225-6463/$ © 2023 ETRI.

iCompass at WANLP 2022 Shared Task: ARBERT and MARBERT for Multilabel Propaganda Classification of Arabic Tweets

Propaganda content has seen massive spread in the biggest social media networks. Major global events such as Covid-19, presidential elections, and wars have all been infested with various propaganda techniques. In participation in the WANLP 2022 Shared Task(Alam et al., 2022), this paper provides a detailed overview of our machine learning system for propaganda techniques classification and its achieved results. The task was carried out using pre-trained transformer based models: ARBERT and MARBERT. The models were fine-tuned for the downstream task in hand: multilabel classification of Arabic tweets. According to the results, MARBERT and ARBERT attained 0.562 and 0.567 micro F1-score on the development set of subtask 1. The submitted model was MARBERT which attained a 0.597 micro F1-score and got the fifth rank. © 2022 Association for Computational Linguistics.

Dramatic Increase in Fear-Related Discussion on Twitter during COVID-19: Analysis, Topic Modeling and Tweets Classification

COVID-19 is one the most lethal virus, causing millions of death to date. It was initially detected in Wuhan, China. It then spread rapidly around the globe, which resultantly created major setbacks in the public health sector. The reason of millions of deaths is not only due to the virus itself but it is also linked to peoples' mental state, and sentiments triggered by the fear of the virus. These sentiments are predominantly available on posts/tweets on social media. This paper presents a novel approach for exploratory data analysis of twitter to understand the emotions of general public;country wise, and user wise. Firstly K-Means clustering is employed for topic modeling to categorize the emotions in each tweet. Further supervised machine learning techniques are used to categorize the multi-label tweets. This research concluded that Fear was the most common emotion in twitter discussion. Furthermore, we classified the dataset by performing decision tree (DT), logistic regression (LR), and support vector machine (SVM), finally this paper concluded the results of classification, which shows that SVM can attain better classification accuracy (99%) for COVID-19 text classification. © 2022 IEEE.

NLP-based feature extraction for the detection of COVID-19 misinformation videos on YouTube

We present a simple NLP methodology for detecting COVID-19 misinformation videos on YouTube by leveraging user comments. We use transfer learning pre-trained models to generate a multi-label classifier that can categorize conspiratorial content. We use the percentage of misinformation comments on each video as a new feature for video classification. We show that the inclusion of this feature in simple models yields an accuracy of up to 82.2%. Furthermore, we verify the significance of the feature by performing a Bayesian analysis. Finally, we show that adding the first hundred comments as tf-idf features increases the video classifier accuracy by up to 89.4%. © ACL 2020.All right reserved.

A Comparative Survey of Multimodal Multilabel Sentiment Analysis and Its Applications Initiated Due to the Impact of COVID-19

This study presents a detailed survey of different works related to sentiment analysis. The COVID-19 pandemic and its impact on people's mental health act as the driving force behind this survey. The survey can help study sentiment analysis and approaches taken in many studies to detect human emotions via advanced technology. It can also help in improving present systems by finding loopholes and increasing their accuracy. Various lexicon and ML-based systems and models like Word2Vec and LSTM were studied in the surveyed papers. Some of the current and future directions highlighted were Twitter sentiment analysis, review-based market analysis, determining changing behavior and emotions in a given time period, and detecting the mental health of employees, and students. This survey provides details related to trends and topics in sentiment analysis and an in-depth understanding of various technologies used in different studies. It also gives an insight into the wide variety of applications related to sentiment analysis. © 2022 IEEE.

Deep Learning Based Multi-Label Prediction of Hospitalization for COVID-19 Cases

Health informatics is an interdisciplinary area where computer science and related disciplines meet to address problems and support healthcare and medicine. In particular, computer has played an important role in medicine. Many existing computer-based systems (e.g., machine learning models) for healthcare applications produce binary prediction (e.g., whether a patient catches a disease or not). However, there are situations in which a non-binary prediction (e.g., what is hospitalization status of a patient) is needed. As a concrete example, over the past two years, people around the world have been affected by the coronavirus disease 2019 (COVID-19) pandemic. There have been works on binary prediction to determine whether a patient is COVID-19 positive or not. With availability of alternative methods (e.g., rapid test), such a binary prediction has become less important. Moreover, with the evolution of the disease (e.g., recent development of COVID-19 Omicron variant), multi-label prediction of the hospitalization status has become more important when compared with binary prediction on the confirmation of cases. Hence, in this paper, we present a multi-label prediction system for computer-based medical applications. Our system makes use of autoencoders (consisting of encoders and decoders) and few-shot learning to predict the hospitalization status (e.g., ICU, semi-ICU, regular wards, or no hospitalization). The prediction is important for allocation of medical resources (e.g., hospital facilities and medical staff), which in turn affect patient lives. Experimental results on real-life open datasets show that, when training with only a few data, our multilabel prediction system gave a high F1-score when predicting hospitalization status of COVID-19 cases. © 2022 IEEE.

A Multilingual Approach to Identify and Classify Exceptional Measures Against COVID-19

The COVID-19 pandemic has witnessed the implementations of exceptional measures by governments across the world to counteract its impact. This work presents the initial results of an on-going project, EXCEPTIUS, aiming to automatically identify, classify and compare exceptional measures against COVID-19 across 32 countries in Europe. To this goal, we created a corpus of legal documents with sentence-level annotations of eight different classes of exceptional measures that are implemented across these countries. We evaluated multiple multi-label classifiers on a manually annotated corpus at sentence level. The XLM-RoBERTa model achieves highest performance on this multilingual multi-label classification task, with a macro-average F1 score of 59.8%. © 2021 Association for Computational Linguistics.

Multi-label topic classification for COVID-19 literature annotation: A BioBERT-based feature enhancement approach

With the rapid expansion and exponential growth of biomedical literatures, especially in the current environment of COVID-19 pandemic, it is urgent to explore an effective technology to automatically manage and categorize massive information for biomedical texts. The wide application and powerful performance of BERT have shown promising results in the field of natural language processing. Thus, we first choose the improved pre-trained language models CovidBERT and BioBERT as the basis, from the best performance of which further enhances semantic representation of with extra title information. Finally, a novel feature enhancement method is proposed to exploit and integrate the distribution of label information effectively. The experimental results show that our model achieves an instance-based F1 score, precision and recall of 93.94%, 93.5% and 94.38% in the task of multi-label topic classification from track 5 BioCreative VII. © VDE VERLAG GMBH - Berlin - Offenbach.

MeVer team tackling Corona virus and Conspiracies using Ensemble Classification

This paper presents the approach developed by the Media Verification (MeVer) team to tackle the task of Corona Virus and Conspiracies Multimedia Analysis Task at the MediaEval 2021 Challenge. We utilized ensemble learning and propose a two-stage classification approach that aims to overcome the challenge of the imbalanced and relatively small training dataset. We deal with the problem as binary classification in the first stage and in the second stage we predict the multi-labels. We experimented with fine-tuning pre-trained Bidirectional Encoder Representations from Transformers (BERT) and achieved a score of 0.294 in terms of the Matthews Correlation Coefficient (MCC), which is the official evaluation metric of the task. Additionally, leveraging on the proposed two-stage classification approach, we extracted a set of feature representations (BoW, TfIDF, embeddings) and classify them using traditional machine learning algorithms (Support Vector Machines, Logistic Regression) achieving in the best run a score of 0.292 of MCC. Copyright 2021 for this paper by its authors.

CAVES: A Dataset to facilitate Explainable Classification and Summarization of Concerns towards COVID Vaccines

Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis to understand the specific concerns that people have towards these vaccines, such as potential side-effects, ineffectiveness, political factors, and so on. Though there are datasets that broadly classify social media posts into Anti-vax and Pro-Vax labels, there is no dataset (to our knowledge) that labels social media posts according to the specific anti-vaccine concerns mentioned in the posts. In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets. We also perform preliminary experiments on the dataset and show that this is a very challenging dataset for multi-label explainable classification and tweet summarization, as is evident by the moderate scores achieved by some state-of-the-art models. © 2022 ACM.

Multilabel Emotion Recognition Technique considering the Characteristics of Unstructured Conversation Data

Due to the recent increase in non-face-To-face services due to COVID-19, the number of users communicating through messengers or SNS (social networking service) is increasing. As a large amount of data is generated by users, research on recognizing emotions by analyzing user information or opinions is being actively conducted. Conversation data such as SNS is freely created by users, so there is no set format. Due to these characteristics, it is difficult to analyze using AI (artificial intelligence), which leads to a decrease in the performance of the emotion recognition technique. Therefore, a processing method suitable for the characteristics of unstructured data is required. Among the unstructured data, most emotion recognition in Korean conversation recognizes a single emotion by analyzing emotion keywords or vocabulary. However, since multiple emotions exist complexly in a single sentence, research on multilabel emotion recognition is needed. Therefore, in this paper, the characteristics of unstructured conversation data are considered and processed for more accurate emotion recognition. In addition, we propose a multilabel emotion recognition technique that understands the meaning of dialogue and recognizes inherent and complex emotions. A deep learning model was compared and tested as a method to verify the usefulness of the proposed technique. As a result, performance was improved when it was processed in consideration of the characteristics of unstructured conversation data. Also, when the attention model was used, accuracy showed the best performance with 65.9%. The proposed technique can contribute to improving the accuracy and performance of conversational emotion recognition. © 2022 Myungjin Lim et al.

Predicting COVID-19 Patient Shielding: A Comprehensive Study

There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention. This study focuses on predicting COVID-19 patient shielding—identifying and protecting patients who are clinically extremely vulnerable from coronavirus. This study focuses on techniques used for the multi-label classification of medical text. Using the information published by the United Kingdom NHS and the World Health Organisation, we present a novel approach to predicting COVID-19 patient shielding as a multi-label classification problem. We use publicly available, de-identified ICU medical text data for our experiments. The labels are derived from the published COVID-19 patient shielding data. We present an extensive comparison across 12 multi-label classifiers from the simple binary relevance to neural networks and the most recent transformers. To the best of our knowledge this is the first comprehensive study, where such a range of multi-label classifiers for medical text are considered. We highlight the benefits of various approaches, and argue that, for the task at hand, both predictive accuracy and processing time are essential. © 2022, Springer Nature Switzerland AG.