Sentiment Analysis in Social Media: A Deep Learning Framework for Analyzing Public Sentiment toward Policies during Natural Disasters - FOOLAD, MOHAMED ABDULLA (2023)
The primary aim of this study is to produce and present a robust, reliable, and accurate framework to measure and analyze public sentiments for decision-makers and policies legislators while managing and administrating the spread of a pandemic or a natural disaster in general. The study utilizes COVID-19 as an actual case study to evaluate the proposed framework, and tests relevant hypotheses. The study used word embedding as a feature vector and then tested the accuracy of different models such as LSTM, Transformers, Logistic regression, Random Forest Classifier, KNN, and Multinomial Naïve Bayes. This work used a deep learning model –LSTM model on the collected tweets to classify the emotions and reactions. The models applied in the classification of COVID-19 global data performed differently with regard to accuracy and precision. Transformers emerged as the most accurate model, with a precision score of (90.18%) and an accuracy of (90.17%). LSTM was also superior with regard to the accuracy, with an (88.45%) precision score and an accuracy of (88.44%). The Random Forest classifier performed fairly, with a (52.43%) precision score and (51.26%). The other models performed poorly in predicting sentiments; for instance, Multinomial Naïve Bayes recorded (43.54%) precision and (41.23%) prediction accuracy, the logistic regression model recorded a (43.54%) precision and (41.23%) prediction accuracy, and the KNN model had (41.23%) precision and (41.11%) prediction accuracy. Overall, LSTM was established as the most suitable model for the selected dataset since it was able to fit the dataset and can be generalized to the current and new datasets, unlike transformers.