A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset

• URL: http://arxiv.org/abs/2605.04888v1
• Date: Wed, 06 May 2026 13:21:07 GMT
• Title: A Comparative Analysis of Machine Learning and Deep Learning Models for Tweet Sentiment Classification: A Case Study on the Sentiment140 Dataset
• Abstract summary: This study compares a Logistic Regression model using TF-IDF features with a deep learning Bidirectional Long Short-Term Memory architecture on a 10,000-tweet subset of the Sentiment140 dataset.<n> Experimental results show that Logistic Regression outperformed BiLSTM, achieving an accuracy of 73.5% compared with 69.17%, while the deep learning model exhibited mild overfitting.
• Score: 1.125921430041183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This study compares a traditional Logistic Regression model using TF-IDF features with a deep learning Bidirectional Long Short-Term Memory (BiLSTM) architecture on a 10,000-tweet subset of the Sentiment140 dataset. Experimental results show that Logistic Regression outperformed BiLSTM, achieving an accuracy of 73.5% compared with 69.17%, while the deep learning model exhibited mild overfitting. These findings suggest that for medium-scale informal text data, classical machine learning with robust feature extraction can outperform more complex deep learning approaches. Finally, the trained models were integrated into an interactive web application using Streamlit and deployed on Hugging Face Spaces for public access.

Related papers

• Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models [0.3655021726150367]
  Sentiment analysis on Twitter employs sophisticated computational techniques to categorize tweets into positive, negative, or neutral sentiments.<n>This paper examines the effectiveness of various machine learning and deep learning approaches.<n>It identifies the optimal sentiment analysis model using a Kaggle Twitter dataset that has been preprocessed through tokenization, lemmatization, and stopword elimination.
  arXiv  Detail & Related papers  (2026-07-08T16:32:39Z)
• InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate [68.14572758582081]
  InfoAtlas is a foundation model-like architecture that eliminates the need for iterative optimization.<n>It learns to identify diverse dependence structures and predict MI directly from the dataset.<n>Experiments demonstrate that InfoAtlas matches state-of-the-art neural estimators in accuracy while achieving $100times$ speedup.
  arXiv  Detail & Related papers  (2026-05-29T18:16:51Z)
• A Comparative Analysis of Classical Machine Learning and Deep Learning Approaches for Sentiment Classification on IMDb Movie Reviews [0.0]
  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.
  arXiv  Detail & Related papers  (2026-05-08T14:44:36Z)
• Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions [59.49967357689445]
  Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies.<n>This study presents a complete open-source dilated real-based framework for the development and validation of deep learning models for affective touch recognition in soft interactive companions.
  arXiv  Detail & Related papers  (2026-04-16T10:20:05Z)
• 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)
• Automated Analysis of Learning Outcomes and Exam Questions Based on Bloom's Taxonomy [0.0]
  This paper explores the automatic classification of exam questions and learning outcomes according to Bloom's taxonomy.<n>A small dataset of 600 sentences labeled with six cognitive categories was processed using traditional machine learning (ML) models.
  arXiv  Detail & Related papers  (2025-11-14T02:31:12Z)
• WebSeer: Training Deeper Search Agents through Reinforcement Learning with Self-Reflection [51.10348385624784]
  We present WebSeer, a more intelligent search agent trained via reinforcement learning enhanced with a self-reflection mechanism.<n>Our approach substantially extends tool-use chains and improves answer accuracy.
  arXiv  Detail & Related papers  (2025-10-21T16:52:00Z)
• 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)
• Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning [77.120955854093]
  We show that data diversity can be a strong predictor of generalization in language models.<n>We introduce G-Vendi, a metric that quantifies diversity via the entropy of model-induced gradients.<n>We present Prismatic Synthesis, a framework for generating diverse synthetic data.
  arXiv  Detail & Related papers  (2025-05-26T16:05:10Z)
• Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks [1.1432909951914676]
  This study introduces a novel approach Multi-Scale Convolutional LSTM with Transfer Learning (TL) to detect anomalies in cellular networks. The model is initially trained from scratch using a publicly available dataset to learn typical network behavior. We compare the performance of the model trained from scratch with that of the fine-tuned model using TL.
  arXiv  Detail & Related papers  (2024-09-30T17:51:54Z)
• Scaling Data Generation in Vision-and-Language Navigation [116.95534559103788]
  We propose an effective paradigm for generating large-scale data for learning. We apply 1200+ photo-realistic environments from HM3D and Gibson datasets and synthesizes 4.9 million instruction trajectory pairs. Thanks to our large-scale dataset, the performance of an existing agent can be pushed up (+11% absolute with regard to previous SoTA) to a significantly new best of 80% single-run success rate on the R2R test split by simple imitation learning.
  arXiv  Detail & Related papers  (2023-07-28T16:03:28Z)
• 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)
• Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data [0.41783829807634776]
  Bidirectional LSTM model achieves an F1 score of 84.58% for Sinhala sentiment analysis. We conclude that it is safe to claim that Facebook reactions are suitable to predict the sentiment of a text.
  arXiv  Detail & Related papers  (2022-01-11T13:31:15Z)
• Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets [4.276883061502341]
  We provide a neural architecture framework for mobile sensing data that can learn generalizable feature representations from time series. This architecture combines benefits from CNN and Trans-former architectures to enable better prediction performance.
  arXiv  Detail & Related papers  (2021-07-09T22:26:50Z)

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.