Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network

Abstract

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.

Worldwide COVID-19 Vaccines Sentiment Analysis Through Twitter Content

“…Das and Kolya [19] proposed a novel strategy for attaining sentiment assessment accuracy on posts on Twitter concerning Coronavirus and future case increase forecasting with the help of a deep neural network. They build a big tweet collection just from Coronavirus tweets.…”

Research on the Art Value and Application of Art Creation Based on the Emotion Analysis of Art

“…Because of the simplicity and efficiency of the algorithm, it is widely used in practical applications. It is to directly establish a model for the possibility of classification, without assuming the data set in advance, so as to avoid the problems caused by the assumption of the distribution error, because it is modeled for the possibility of classification, so it can not only predict out of the category, you can also get the probability of belonging to the category [28].…”

Public Attitudes During the Second Lockdown: Sentiment and Topic Analyses Using Tweets From Ontario, Canada

“…While LDA and VADER have been commonly used for topic modelling and sentiment analysis, respectively, deep learning techniques, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT), have become more successful and have been adopted in more recent literature for sentiment analysis [21][22][23][24]. For example, Chandra and Krishna [21] used deep learning models for COVID-19 tweet sentiment analysis in India from March to September 2021.…”

“…However, deep learning techniques still have some limitations. For instance, supervised deep learning techniques demonstrated in existing literature have been conducted in a supervised manner, and this requires large and correctly labelled training datasets [22][23][24]. Interpretability is another issue because public health professionals may not have deep understanding of deep learning models, which have been more complicated than conventional LDA and VADER.…”

“…Similarly, Imran et al [ 22 ] used a supervised multi-layer LSTM to classify and compare six emotions across Pakistan, India, Norway, Sweden, Canada, and the US [ 22 ]. Das and Kolya [ 23 ] also proposed a supervised deep convolutional neural network (CNN) to evaluate sentiments and predict COVID-19 cases globally since data were retrieved from 15 countries [ 23 ]. Yet they classified emotions expressed in tweets only as either positive or negative [ 23 ].…”