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

TL;DR

This study compares traditional machine learning and deep learning models for tweet sentiment analysis, finding classical methods outperform deep learning on medium-scale informal social media data.

Contribution

It provides a comparative analysis showing classical ML can outperform deep learning in sentiment classification of tweets on the Sentiment140 dataset.

Findings

Logistic Regression achieved 73.5% accuracy, outperforming BiLSTM's 69.17%.

Deep learning model showed mild overfitting.

Models were deployed via Streamlit on Hugging Face Spaces.

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.