A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches for Sentiment Classification on IMDb Movie Reviews

• URL: http://arxiv.org/abs/2605.07811v1
• Date: Fri, 08 May 2026 14:44:36 GMT
• Title: A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches for Sentiment Classification on IMDb Movie Reviews
• Abstract summary: This paper presents a comparative study of classical machine learning and deep learning methods for sentiment classification on the IMDb movie reviews dataset.<n>The machine learning pipeline uses TF-IDF features and PyCaret AutoML to evaluate Logistic Regression, Nave Bayes, and Support Vector Machine.<n>The deep learning pipeline implements BiLSTM and BiLSTM with an attention mechanism.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a comparative study of classical machine learning and deep learning methods for sentiment classification on the IMDb movie reviews dataset. The machine learning pipeline uses TF-IDF features and PyCaret AutoML to evaluate Logistic Regression, Naïve Bayes, and Support Vector Machine, while the deep learning pipeline implements BiLSTM and BiLSTM with an attention mechanism. Experimental results show that classical machine learning, especially SVM, achieves the best performance with an accuracy of 0.8530, outperforming the deep learning models in this study. The BiLSTM with Attention model improves over the standard BiLSTM and reaches an accuracy of 0.706, indicating better contextual modeling. The paper concludes that although deep learning can capture sequential dependencies, classical machine learning remains a strong baseline when combined with effective feature engineering such as TF-IDF, particularly under limited data and computational resources.

Related papers

• A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis [0.0]
  This study presents a comparative performance analysis between traditional machine learning algorithms and deep learning architectures.<n>SVM offers the most optimal balance between predictive precision and processing speed for email detection tasks.
  arXiv  Detail & Related papers  (2026-05-05T07:20:52Z)
• Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models [0.0]
  This paper compares Machine Learning and LSTM-based Deep Learning methods for sentiment analysis of Mobile Legends app reviews.<n>Using a dataset of 10,000 reviews labeled as positive, negative, and neutral, the study evaluates traditional models with TF-IDF and PyCaret AutoML and compares them against an LSTM model designed to capture sequential text dependencies.
  arXiv  Detail & Related papers  (2026-05-02T08:25:59Z)
• AI Generated Text Detection [0.0]
  This paper presents an evaluation of AI text detection methods, including both traditional machine learning models and transformer-based architectures.<n>We utilize two datasets, HC3 and DAIGT v2, to build a unified benchmark and apply a topic-based data split to prevent information leakage.<n>Results indicate that contextual modeling is significantly superior to lexical features and highlight the importance of mitigating topic memorization.
  arXiv  Detail & Related papers  (2026-01-07T11:18:10Z)
• Music Genre Classification Using Machine Learning Techniques [0.0]
  This paper presents a comparative analysis of machine learning methodologies for automatic music genre classification.<n>We evaluate the performance of classical classifiers, including Support Vector Machines (SVM) and ensemble methods, trained on a comprehensive set of hand-crafted audio features.<n>Our findings demonstrate a noteworthy result: the SVM, leveraging domain-specific feature engineering, achieves superior classification accuracy compared to the end-to-end CNN model.
  arXiv  Detail & Related papers  (2025-09-01T20:43:55Z)
• Efficient Machine Unlearning via Influence Approximation [75.31015485113993]
  Influence-based unlearning has emerged as a prominent approach to estimate the impact of individual training samples on model parameters without retraining.<n>This paper establishes a theoretical link between memorizing (incremental learning) and forgetting (unlearning)<n>We introduce the Influence Approximation Unlearning algorithm for efficient machine unlearning from the incremental perspective.
  arXiv  Detail & Related papers  (2025-07-31T05:34:27Z)
• Continual Learning with Pre-Trained Models: A Survey [61.97613090666247]
  Continual Learning aims to overcome the catastrophic forgetting of former knowledge when learning new ones. This paper presents a comprehensive survey of the latest advancements in PTM-based CL.
  arXiv  Detail & Related papers  (2024-01-29T18:27:52Z)
• An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine [0.12289361708127873]
  This paper presents an interpretable review of various machine learning and deep learning models to predict the maintenance of aircraft engine. In this study, sensor data is utilized to predict aircraft engine failure within a predetermined number of cycles using LSTM, Bi-LSTM, RNN, Bi-RNN GRU, Random Forest, KNN, Naive Bayes, and Gradient Boosting. A lucrative accuracy of 97.8%, 97.14%, and 96.42% are achieved by GRU, Bi-LSTM, and LSTM respectively.
  arXiv  Detail & Related papers  (2023-09-23T08:54:10Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.