What does this research mean for the field?

Linear Support Vector Machines and Bernoulli Naive Bayes outperform Logistic Regression in sentiment analysis tasks, achieving approximately 89% accuracy on movie review data. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to evaluate and compare the performance of three machine learning algorithms in sentiment analysis tasks.

March 21, 2026

Evaluating machine learning algorithms for sentiment analysis: a comparative study to support data-driven decision making

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Authors

NDNur Hafiza Mohamad Daud NSNor Hayati Shafii DNDiana Sirmayunie Md Nasir

Discussion

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Implication

Comparative study evaluates algorithm performance in sentiment analysis, suggesting effective choices for data-driven decisions.

Key Points

This research aims to evaluate and compare the performance of three machine learning algorithms in sentiment analysis tasks.
Conducted a comparative analysis of Bernoulli Naive Bayes, Linear Support Vector Machines, and Logistic Regression.
Utilized the IMDb 500k movie reviews dataset for sentiment classification.
Evaluated model accuracy and performance metrics across various domains.
Linear Support Vector Machines and Bernoulli Naive Bayes achieved the highest accuracy at 89%.
Linear SVM slightly outperforms Bernoulli Naive Bayes in different performance metrics.
Logistic Regression has the lowest accuracy among the three algorithms.

Cite This Study

Daud et al. (2025) studied this question.

synapsesocial.com/papers/69be36bf6e48c4981c675d45 https://doi.org/10.24191/ji.v20i2.6492

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Evaluating machine learning algorithms for sentiment analysis: a comparative study to support data-driven decision making

Discussion

Member takes

Implication

Key Points

Cite This Study

Evaluating machine learning algorithms for sentiment analysis: a comparative study to support data-driven decision making

Discussion

Member takes

Implication

Key Points

Cite This Study

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