Sentiment Analysis Applied on Tweets

As digitization continues to expand nowadays, the accurate capture and comprehension of public sentiment on social media has become vital for various stakeholders such as government, businesses, and researchers. In this paper, we aim to perform sentiment analysis on Tweets and explore effective methods to classify sentiment into joy, sadness, anger, and fear from textual content. We utilized datasets from Kaggle containing textual tweets and employed models such as RNN, LSTM, Transformer, and SVM to compare their performance in sentiment analysis. The findings of this paper may serve as a valuable reference for the selection of models in sentiment analysis, particularly when working with medium-sized datasets. Additionally, they may offer guidance on selecting universally applicable model choices for conducting sentiment analysis across social media platforms.