Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme

Abstract

The current population worldwide extensively uses social media to share thoughts, societal issues, and personal concerns. Social media can be viewed as an intelligent platform that can be augmented with a capability to analyze and predict various issues such as business needs, environmental needs, election trends (polls), governmental needs, etc. This has motivated us to initiate a comprehensive search of the COVID-19 pandemic-related views and opinions amongst the population on Twitter. The basic training data have been collected from Twitter posts. On this basis, we have developed research involving ensemble deep learning techniques to reach a better prediction of the future evolutions of views in Twitter when compared to previous works that do the same. First, feature extraction is performed through an N-gram stacked autoencoder supervised learning algorithm. The extracted features are then involved in a classification and prediction involving an ensemble fusion scheme of selected machine learning techniques such as decision tree (DT), support vector machine (SVM), random forest (RF), and K-nearest neighbour (KNN). all individual results are combined/fused for a better prediction by using both mean and mode techniques. Our proposed scheme of an N-gram stacked encoder integrated in an ensemble machine learning scheme outperforms all the other existing competing techniques such unigram autoencoder, bigram autoencoder, etc. Our experimental results have been obtained from a comprehensive evaluation involving a dataset extracted from open-source data available from Twitter that were filtered by using the keywords “covid”, “covid19”, “coronavirus”, “covid-19”, “sarscov2”, and “covid_19”.

Comparison of Machine Learning and Sentiment Analysis in Detection of Suspicious Online Reviewers on Different Type of Data

“…Sentiment analysis can be provided using again the machine learning approach and a lexicon-based approach. There are some works, which have used machine learning approach to sentiment analysis for example [ 25 ] have developed Ensemble Learning Scheme using DT, SVM, RF and KNN (K-Nearest Neighbours) for sentiment analysis of COVID-19 related comments. In work [ 26 ] deep learning models for sentiment analysis were used in recommender systems.…”

A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

“…Similarly, Kandasamy et al [166] proposed an ensemble deep learning model to analyze COVID-19-related senti-ments and opinions among Twitter users. The study used the ensemble model to obtain better predictions than previous studies.…”

Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

“…The input image from the dataset is preprocessed with Gabor Filter based Gaussian filter for noise removal and enhanced the image to improve the feature extraction and classification process. Gabor filter is a multi-resolution filter [20][21][22][23][24][25][26][27] for the removal of noise. It can be applied in various orientations and frequencies [28][29][30][31][32][33][34][35][36][37].…”