Sentiment Analysis of Textual Reviews Using Deep Learning Techniques

Abstract

Opinion mining, another name for sentiment analysis, is a computer strategy that seeks to automatically extract and categorize sentiment from textual data. With the exponential growth of online platforms and social media, analyzing and understanding user sentiments expressed in reviews has become increasingly important for businesses and researchers. Deep learning techniques, specifically neural networks, have shown remarkable success in natural language processing tasks, including sentiment analysis. This study explores multiple methodologies, datasets, and assessment criteria to provide a thorough examination of sentiment analysis utilizing deep learning algorithms. The paper presents an overview of deep learning architectures commonly employed for sentiment analysis, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers. Furthermore, it discusses pre-processing techniques, feature representation methods, and the impact of data imbalance on sentiment classification performance. The paper also investigates different approaches for model training, including supervised, semi-supervised, and transfer learning. The study analyzes current benchmarks, performance evaluation criteria, and future perspectives for deep learning-based sentiment analysis research.