Deep Learning Techniques for Sentiment Analysis

This document is a technical report that discusses the application of deep learning techniques in sentiment analysis and opinion mining. It explains how sentiment analysis, which quantifies attitudes and emotions associated with various subjects, has evolved from traditional methods to more advanced approaches using deep learning. The report outlines the limitations of classical sentiment analysis, which typically relies on rules-based or corpus-based methods that often lack accuracy and context. It details how deep learning, particularly through the use of Recursive Neural Networks (RNN) and Long Short-Term Memory (LSTM) architectures, significantly enhances the accuracy and speed of sentiment analysis. The report also highlights the practical applications of these technologies in real-time analysis, particularly in understanding consumer sentiments on social media platforms. It emphasizes the importance of context in sentiment analysis and how deep learning can provide a more nuanced understanding of public opinion, thereby improving brand reputation assessment.