Exploring the Effectiveness of Machine Learning and Deep Learning Algorithms for Sentiment Analysis: A Systematic Literature Review

Abstract

Sentiment Analysis, a significant domain within Natural Language Processing (NLP), focuses on extracting and interpreting subjective information—such as emotions, opinions, and attitudes—from textual data. With the increasing volume of user-generated content on social media and digital platforms, sentiment analysis has become essential for deriving actionable insights across various sectors. This study presents a systematic literature review of sentiment analysis methodologies, encompassing traditional machine learning algorithms, lexicon-based approaches, and recent advancements in deep learning techniques.The review follows a structured protocol comprising three phases: planning, execution, and analysis/reporting. During the execution phase, 67 peer-reviewed articles were initially retrieved, with 25 meeting predefined inclusion and exclusion criteria. The analysis phase involved a detailed examination of each study’s methodology, experimental setup, and key contributions. Among the deep learning models evaluated, Long Short-Term Memory (LSTM) networks were identified as the most frequently adopted architecture for sentiment classification tasks. This review highlights current trends, technical challenges, and emerging opportunities in the field, providing valuable guidance for future research and development in applications such as market analysis, public health monitoring, financial forecasting, and crisis management.