Review of Twitter Sentiment Analysis using Deep Learning Approaches

Abstract

Twitter has emerged as one of the most popular social media platforms where users frequently share their opinions, emotions, and perspectives on a wide range of topics, including politics, products, events, and social issues. Analyzing these opinions is important for understanding public sentiment and assisting decision-making processes across various domains. Sentiment analysis, also referred to as opinion mining, is a natural language processing (NLP) technique used to detect and categorize the sentiment expressed in textual data, typically classifying it as positive, negative, or neutral. Social media platforms such as Twitter produce a vast amount of user-generated content that captures public opinions, emotions, and attitudes regarding events, products, and policies. Sentiment analysis of Twitter data has become an important research area for understanding public perception in real time. This study conducts an empirical evaluation of Twitter sentiment classification using deep learning techniques. Several deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), are applied to preprocessed Twitter datasets. These models are trained to categorize tweets into positive, negative, and neutral sentiment classes. The experimental results indicate that deep learning methods outperform conventional machine learning approaches by better capturing contextual and semantic features within textual data. The results emphasize the capability of deep learning techniques to enhance the accuracy and robustness of sentiment classification in large-scale Twitter data analysis.