Ref

Contribution

Dataset

Algorithms

Best Accuracy

This Work

An examination at how lexicon-based and deep learning-based approaches to Twitter sentiment analysis vary

Sentiment140 dataset containing 1.6 million tweets.

Lexicon-based approach and Long Short-Term Memory (LSTM) in deep learning

98% (LSTM)

[11]

Twitter sentiment analysis in Hausa with the use of machine learning and lexicon-based techniques

BBC Hausa Twitter API.

Logistic Regression, Multinomial Naive Bayes (MNB) with Count Vectorizer and TF-IDF

86% (LR)

[12]

Real-time sentiment analysis using SVM, K-SVM, and Multinomial Naive Bayes.

Twitter data and contested territory news websites.

Support Vector Machine (SVM), Kernel SVM (K-SVM), Multinomial Naive Bayes

80% (K-SVM)

[13]

Comparative Research of Lexicon and Machine Learning Techniques for Sentiment Analysis

TF-IDF

Logistic regression, support vector machine (SVM), logistic regression, AFINN, and Vader lexicon

96.3% (SVM)

[15]

Analysis of Twitter users’ COVID-19 attitudes using machine learning.

COVID-19 tweets from Kaggle.

Naive Bayes, Random Forest, Support Vector Machine, Neural Network

79.37% (Random Forest)

[17]

Using deep learning and lexicons, Indian tweets on COVID-19 and vaccination are analyzed.

COVID-19 and vaccination tweets from India.

Recurrent Neural Network (RNN) with Bi-LSTM and GRU techniques

93.03% (GRU)