Natural Language Processing (NLP) for Sentiment Analysis: A Comparative Study of Machine Learning Algorithms

Abstract

Sentiment analysis has emerged as a vital application of Natural Language
Processing (NLP), enabling the extraction of subjective information from textual
data. This study conducts a comparative analysis of various machine learning
algorithms employed in sentiment analysis, including traditional models such
as Naïve Bayes, Support Vector Machines (SVM), and Decision Trees, as well as
contemporary techniques such as Random Forest, Gradient Boosting, and deep
learning approaches like Recurrent Neural Networks (RNN) and Long Short-
Term Memory (LSTM) networks. Using a comprehensive dataset sourced from
social media platforms and product reviews, we evaluate the performance of
these algorithms based on accuracy, precision, recall, and F1-score. Our findings
highlight the strengths and weaknesses of each algorithm in handling sentiment
classification tasks, emphasizing the influence of feature extraction techniques,
such as Bag of Words and Word Embeddings, on model performance. The results
indicate that while deep learning models generally outperform traditional
algorithms, the choice of algorithm should be tailored to the specific context
and requirements of the analysis. This study contributes to the ongoing discourse
on the efficacy of machine learning methods in NLP, offering insights that can
guide researchers and practitioners in selecting appropriate algorithms for
sentiment analysis tasks.