Sentiment analysis of COVID-19 social media data through machine learning

Abstract

Pandemics are a severe threat to lives in the universe and our universe encounters several pandemics till now. COVID-19 is one of them, which is a viral infectious disease that increased morbidity and mortality worldwide. This has a negative impact on countries’ economies, as well as social and political concerns throughout the world. The growths of social media have witnessed much pandemic-related news and are shared by many groups of people. This social media news was also helpful to analyze the effects of the pandemic clearly. Twitter is one of the social media networks where people shared COVID-19 related news in a wider range. Meanwhile, several approaches have been proposed to analyze the COVID-19 related sentimental analysis. To enhance the accuracy of sentimental analysis, we have proposed a novel approach known as Sentimental Analysis of Twitter social media Data (SATD). Our proposed method is based on five different machine learning models such as Logistic Regression, Random Forest Classifier, Multinomial NB Classifier, Support Vector Machine, and Decision Tree Classifier. These five classifiers possess various advantages and hence the proposed approach effectively classifies the tweets from the Twint. Experimental analyses are made and these classifier models are used to calculate different values such as precision, recall, f1-score, and support. Moreover, the results are also represented as a confusion matrix, accuracy, precision, and receiver operating characteristic (ROC) graphs. From the experimental and discussion section, it is obtained that the accuracy of our proposed classifier model is high.

Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning

“…Four filling methods and three feature screening methods were used to obtain 12 datasets. Eighteen machine learning algorithms, including logistic regression [27, 28], Latent Dirichlet allocation [29], Quadratic Discriminant Analysis [30], Stochastic Gradient Descent [31], k-Nearest Neighbor [32], Decision Tree [33], Naive Bayes [34], Gaussian Naïve Bayes [35], Multinomial Naive Bayes [36], Bernoulli Naïve Bayes [37], Support Vector Machine [38], passive-aggressive [39], AdaBoost [40], bagging, Random Forest [41], Extremely Randomized Trees [42], gradient boosting [41], XGBoost [43], and ensemble learning [44], were used to train 216 models. The process of building the models was as follows:The dataset was randomly divided into a training and a test set in a ratio of 8:2.The training set data were entered into the machine learning model, and the 10-fold cross-validation method was used to continuously adjust the model parameters, so that the parameters had the largest area under the receiver operating characteristic curve (AUC) value on the training set.…”

SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation

“…For example, [2] compared three ML algorithms, including SVM, RF, and NB, on the Amazon product review dataset, and POS-tagging was used as a sentence representation. Another recently conducted research study investigated the efficacy of five different ML algorithms, namely RF, LR, SVM, NB, and DT in identifying the sentiments present in tweets about COVID-19 [20].…”

“…To achieve better model performance for such a dataset, DL-based models have the potential to extract better features than traditional MLbased models, has recently gained a lot of attention. Convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent units (GRU) architectures have been studied, as has been evidenced by the existing range of published works [20]- [22]. CNN [23] has performed admirably with image data.…”

Deterministic solution of algebraic equations in sentiment analysis

“…A large number of political [81], social [34], economic [29], health [17,50], educational [82], and military groups are interested in obtaining the opinion of customers to modify or improve their services. For example, the information obtained from opinion mining helps financial companies to identify the risks related to new investments [59].…”