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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM37951

Comparative Analysis of Machine Learning Techniques for Sentiment Classification

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM37951

Volume:

8 |

10 |

Oct 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM37951

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PPJ

Prof. P.J. Jambhulkar

TA

Tejasvi Alur

RK

Ritanshu Koul

KG

Khushi Gandhi

RN

Raj Nathani

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Abstract

Sentiment analysis is an important part of natural language processing (NLP) and has many applications in social media, e-commerce, and other fields. This study aims to provide a clear distinction between these methodologies by offering a structured overview of sentiment analysis and the variety of techniques used in its execution. The article examines different machine learning algorithms for sentiment analysis and high- lights their advantages and disadvantages by drawing on credible prior research on the subject. Additionally, the study provides a tabular comparison of different machine learning methods by selecting suitable parameters.

KEYWORDS

Decision Tree Support Vector Machine (SVM) Random Forest Convolution neural network (CNN) Neural network Long Short-Term Memory Networks (LSTM) BERT

Cite This Article

Prof. P.J. Jambhulkar, Tejasvi Alur, Ritanshu Koul, Khushi Gandhi, Raj Nathani, "Comparative Analysis of Machine Learning Techniques for Sentiment Classification," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 10, Oct 2024. https://doi.org/10.55041/IJSREM37951

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Publication Date

10/14/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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