Twitter Sentiment Analysis Benchmarking using Transformer-based and Traditional Machine Learning Models

1. Introduction Sentiment analysis, also known as opinion mining, is a vital and widely studied task in the field of Natural Language Processing (NLP). It aims to extract and classify the underlying sentiment or emotional tone expressed in a piece of text. This process enables systems to understand whether the opinion conveyed is positive, negative, or neutral. The applications of sentiment analysis are vast and include critical domains such as: Product Review Analysis: Helping businesses understand customer satisfaction and areas for improvement. Social Media Monitoring: Tracking public opinion about brands, political movements, or global events. Customer Feedback Systems: Automating feedback analysis to drive business decisions and service improvements. Traditionally, sentiment analysis relied heavily on classical machine learning approaches such as Logistic Regression, Naive Bayes, and Support Vector Machines. These models depend on hand-crafted features, such as word frequencies or n-grams, and require significant feature engineering to perform well. Although effective to a degree, these models struggle with context understanding and nuanced language. With the rise of deep learning and transformer architectures, especially models like BERT, RoBERTa, and DistilBERT, sentiment analysis has entered a new era. Transformer models are pre-trained on large-scale text corpora and are capable of capturing complex semantic and syntactic relationships in language. These models achieve state-of-the-art performance on various NLP tasks, including sentiment classification, due to their attention mechanisms and contextual embeddings. This report focuses on benchmarking and comparing the performance of both traditional and transformer-based models on a sentiment-labeled dataset. The goal is to evaluate the strengths and weaknesses of each model category and recommend the best-performing model based on key metrics such as accuracy, precision, recall, and F1 score. 2. Objective The primary objective of this project is to conduct a comprehensive benchmarking study of various sentiment analysis models, comparing traditional machine learning techniques with modern transformer-based deep learning approaches. The goals are structured as follows: Model Performance Comparison Assess and compare the performance of several traditional machine learning models—Logistic Regression, Multinomial Naive Bayes, and Linear Support Vector Classifier (SVC)—against state-of-the-art transformer-based models, namely DistilBERT, BERT (Multilingual), and RoBERTa. These models represent two distinct paradigms in NLP: one relying on statistical learning and the other on deep contextual language understanding. Evaluation Using Standard Metrics Evaluate each model using standard classification performance metrics such as: Accuracy – How often the model predicts correctly. Precision – The proportion of positive identifications that were actually correct. Recall – The proportion of actual positives that were correctly identified. F1 Score – The harmonic mean of precision and recall, providing a balance between the two. Model Recommendation Based on empirical results, the project aims to recommend the most effective model for sentiment classification, highlighting trade-offs between accuracy, complexity, and inference efficiency. 3. Tools & Technologies Used To implement and evaluate the sentiment analysis models effectively, a variety of tools and libraries were utilized. Each tool played a specific role in the data pipeline, model building, evaluation, and overall development environment: Tool / Library Purpose Python The primary programming language used for scripting and logic implementation. pandas Data manipulation and preprocessing, including reading CSV files and handling datasets. scikit-learn (sklearn) Used for implementing traditional ML models, vectorizing text, and computing evaluation metrics. transformers Hugging Face library providing access to pre-trained transformer models for NLP. torch (PyTorch) Backend deep learning framework used by transformer models for model inference. tqdm Utility for displaying progress bars during model inference loops. Visual Studio Code (VS Code) Source-code editor used for development and testing of the project. 4. Dataset Description The dataset employed in this project is a structured CSV (Comma-Separated Values) file named data.csv. It is designed to support a binary sentiment classification task, which aims to categorize input texts into one of two sentiments: Positive (1) or Negative (0). Structure of the Dataset The dataset contains the following key columns: text: This column includes the raw textual data to be analyzed. It may consist of short-form content such as tweets, product reviews, or user comments. The text entries vary in length and tone, presenting a realistic challenge

Apr 16, 2025 - 06:03

Twitter Sentiment Analysis Benchmarking using Transformer-based and Traditional Machine Learning Models

1. Introduction

Sentiment analysis, also known as opinion mining, is a vital and widely studied task in the field of Natural Language Processing (NLP). It aims to extract and classify the underlying sentiment or emotional tone expressed in a piece of text. This process enables systems to understand whether the opinion conveyed is positive, negative, or neutral. The applications of sentiment analysis are vast and include critical domains such as:

Product Review Analysis: Helping businesses understand customer satisfaction and areas for improvement.
Social Media Monitoring: Tracking public opinion about brands, political movements, or global events.
Customer Feedback Systems: Automating feedback analysis to drive business decisions and service improvements.

Traditionally, sentiment analysis relied heavily on classical machine learning approaches such as Logistic Regression, Naive Bayes, and Support Vector Machines. These models depend on hand-crafted features, such as word frequencies or n-grams, and require significant feature engineering to perform well. Although effective to a degree, these models struggle with context understanding and nuanced language.

With the rise of deep learning and transformer architectures, especially models like BERT, RoBERTa, and DistilBERT, sentiment analysis has entered a new era. Transformer models are pre-trained on large-scale text corpora and are capable of capturing complex semantic and syntactic relationships in language. These models achieve state-of-the-art performance on various NLP tasks, including sentiment classification, due to their attention mechanisms and contextual embeddings.

This report focuses on benchmarking and comparing the performance of both traditional and transformer-based models on a sentiment-labeled dataset. The goal is to evaluate the strengths and weaknesses of each model category and recommend the best-performing model based on key metrics such as accuracy, precision, recall, and F1 score.

2. Objective

The primary objective of this project is to conduct a comprehensive benchmarking study of various sentiment analysis models, comparing traditional machine learning techniques with modern transformer-based deep learning approaches. The goals are structured as follows:

Model Performance Comparison

Assess and compare the performance of several traditional machine learning models—Logistic Regression, Multinomial Naive Bayes, and Linear Support Vector Classifier (SVC)—against state-of-the-art transformer-based models, namely DistilBERT, BERT (Multilingual), and RoBERTa. These models represent two distinct paradigms in NLP: one relying on statistical learning and the other on deep contextual language understanding.
Evaluation Using Standard Metrics

Evaluate each model using standard classification performance metrics such as:
- Accuracy – How often the model predicts correctly.
- Precision – The proportion of positive identifications that were actually correct.
- Recall – The proportion of actual positives that were correctly identified.
- F1 Score – The harmonic mean of precision and recall, providing a balance between the two.
Model Recommendation

Based on empirical results, the project aims to recommend the most effective model for sentiment classification, highlighting trade-offs between accuracy, complexity, and inference efficiency.

3. Tools & Technologies Used

To implement and evaluate the sentiment analysis models effectively, a variety of tools and libraries were utilized. Each tool played a specific role in the data pipeline, model building, evaluation, and overall development environment:

Tool / Library	Purpose
Python	The primary programming language used for scripting and logic implementation.
pandas	Data manipulation and preprocessing, including reading CSV files and handling datasets.
scikit-learn (sklearn)	Used for implementing traditional ML models, vectorizing text, and computing evaluation metrics.
transformers	Hugging Face library providing access to pre-trained transformer models for NLP.
torch (PyTorch)	Backend deep learning framework used by transformer models for model inference.
tqdm	Utility for displaying progress bars during model inference loops.
Visual Studio Code (VS Code)	Source-code editor used for development and testing of the project.

4. Dataset Description

The dataset employed in this project is a structured CSV (Comma-Separated Values) file named data.csv. It is designed to support a binary sentiment classification task, which aims to categorize input texts into one of two sentiments: Positive (1) or Negative (0).

Structure of the Dataset

The dataset contains the following key columns:

text:

This column includes the raw textual data to be analyzed. It may consist of short-form content such as tweets, product reviews, or user comments. The text entries vary in length and tone, presenting a realistic challenge for classification models in understanding and interpreting human language.
label:

This column contains the sentiment annotations corresponding to each text entry. The values are:
- 0 – Represents a Negative sentiment.
- 1 – Represents a Positive sentiment.

This simple binary classification format is widely used in sentiment analysis research and is compatible with a variety of machine learning and deep learning models.

Dataset Characteristics

Size: (Optional: Mention the number of records here if known, e.g., “The dataset consists of 5,000 labeled samples.”)
Class Balance: (Optional: You may analyze how balanced the number of positive vs negative samples are—add a pie chart if applicable.)
Language: The dataset is assumed to be in English, making it well-suited for the pre-trained models used in this project, most of which are trained on English corpora.

Suitability for Model Evaluation

The dataset provides a consistent platform for evaluating the performance of both traditional and modern NLP models. By keeping the format uniform and simple, we ensure that:

Input compatibility is maintained across models (text-only input).
Evaluation metrics remain meaningful and comparable.
Preprocessing requirements are minimized for transformer models, which are capable of handling raw text effectively.

5. Methodology

To ensure clarity and modular design, the overall approach is divided into multiple stages. These steps encompass the configuration, training, inference, and evaluation of both traditional machine learning and transformer-based models on a common dataset.

5.1 Model Configuration

The experiment involved the benchmarking of two main categories of models:

A. Transformer-Based Models

These are pre-trained deep learning models designed for Natural Language Processing tasks and fine-tuned specifically for sentiment analysis. The models selected for this experiment include:

DistilBERT: A distilled version of BERT that has been fine-tuned on the SST-2 dataset for binary sentiment classification.
BERT (Multilingual): Trained on product reviews with sentiment scores from 1 to 5 stars. Predictions are mapped to binary labels (e.g., 4-5 stars → Positive).
RoBERTa (Twitter): Specifically fine-tuned for sentiment analysis on social media data such as tweets, leveraging Twitter’s linguistic patterns.

B. Traditional Machine Learning Models

These classical models are known for their speed and interpretability:

Logistic Regression: A linear model used for binary classification.
Multinomial Naive Bayes: Often used for text classification problems with discrete features (like word counts or TF-IDF values).
Linear Support Vector Classifier (SVC): A linear version of SVM optimized for speed in high-dimensional spaces like TF-IDF vectors.

5.2 Data Loading and Preprocessing

The dataset is imported using the pandas library.
Two columns, namely text and label, are extracted.
Transformer Models: Do not require manual preprocessing; instead, they rely on their internal tokenization mechanisms (such as WordPiece or Byte-Pair Encoding).
Traditional Models: Text data is converted into numerical vectors using TF-IDF Vectorization, with a vocabulary size capped at 5000 features to limit dimensionality.