Natural Language Processing (NLP) for Sentiment Analysis: A Comparative Study of Machine Learning Algorithms
How to cite: Shad, R.; Potter, K.; Gracias, A. Natural Language Processing (NLP) for Sentiment Analysis: A Comparative Study of Machine Learning Algorithms. Preprints 2024, 2024102338. https://doi.org/10.20944/preprints202410.2338.v1 Shad, R.; Potter, K.; Gracias, A. Natural Language Processing (NLP) for Sentiment Analysis: A Comparative Study of Machine Learning Algorithms. Preprints 2024, 2024102338. https://doi.org/10.20944/preprints202410.2338.v1
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.
Keywords
Subject
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)