Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network
| Autor: | Sourav Das, Anup Kumar Kolya |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Text corpus
Predictive analysis Phrase Computer science Cognitive Neuroscience Stability (learning theory) 02 engineering and technology Machine learning computer.software_genre Convolutional neural network Sentiment analysis Mathematics (miscellaneous) Deep convolutional network Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Artificial neural network business.industry Deep learning 020206 networking & telecommunications Coronavirus Test case 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Covid-19 computer Research Paper |
| Zdroj: | Evolutionary Intelligence |
| ISSN: | 1864-5917 1864-5909 |
| Popis: | Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink