A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets.
TL;DR: In this paper, the authors have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores.
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Abstract: With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).
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Citations
Hybrid Deep Learning Models for Sentiment Analysis
TL;DR: The hybrid models increased the accuracy for sentiment analysis compared with single models on all types of datasets, especially the combination of deep learning models with SVM, and the reliability of the latter was significantly higher.
Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets.
TL;DR: In this paper, the authors analyzed people's sentiment based on the classification of tweets collected from the social media platform, Twitter, in Nepal and used three different feature extraction methods-fastText-based (ft), domain-specific (ds), and domain-agnostic (da) for the representation of tweets.
A visual review of artificial intelligence and Industry 4.0 in healthcare
TL;DR: In this article , the authors present a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19, which provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool.
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A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification
TL;DR: The evaluation results on the NepCOV19Tweets show that the hybrid feature extraction method not only outperforms the other two individual feature extraction methods while using nine different machine learning algorithms but also provides excellent performance when compared with the state-of-the-art methods.
Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study.
TL;DR: In this paper, the authors used deep learning and NLP 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).
References
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
- 01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
53.5K
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
12.5K
•Proceedings Article
Recurrent neural network based language model
Tomas Mikolov,Martin Karafiat,Lukas Burget,Jan Cernocký,Sanjeev Khudanpur +4 more
- 01 Jan 2010
TL;DR: Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model.
A Review of Coronavirus Disease-2019 (COVID-19).
TL;DR: The disease is mild in most people; in some (usually the elderly and those with comorbidities), it may progress to pneumonia, acute respiratory distress syndrome (ARDS) and multi organ dysfunction and many people are asymptomatic.