Sentiment Analysis with Naive Bayes: A Step-by-Step NLP Walkthrough

2026-02-28 · Source: Naturallanguageprocessing on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, short

Summary

This article details a complete sentiment analysis pipeline using Python, demonstrating how to classify text as positive, negative, or neutral. It utilizes a Kaggle dataset comprising 31,232 records and foundational libraries like NumPy and Pandas for data handling. The process involves extensive text preprocessing with NLTK, including regular expression cleaning, lowercasing, tokenization, Porter Stemming, and stopword removal. Feature extraction is performed using `CountVectorizer` to convert text into a Bag of Words model, limited to the top 800 most frequent words. The data is then split into 80% training and 20% testing sets, with a Gaussian Naive Bayes classifier trained on the former. Finally, the model's performance is evaluated using a confusion matrix and accuracy score, achieving an accuracy of 65.21% on the test set.

Key takeaway

For AI Engineers building text classification systems, this walkthrough provides a clear, reproducible method for implementing sentiment analysis. You should prioritize robust text preprocessing, including stemming and stopword removal, and consider `CountVectorizer` with `max_features` for efficient feature extraction. This approach offers a solid baseline for projects requiring sentiment classification, allowing you to quickly deploy and evaluate a functional model.

Key insights

A sentiment analysis pipeline uses Naive Bayes, demonstrating text preprocessing and Bag of Words feature extraction.

Principles

• Text preprocessing is critical for NLP performance.
• Bag of Words is an effective text feature representation.

Method

The method involves loading data, preprocessing text (cleaning, lowercasing, stemming, stopword removal), extracting features with `CountVectorizer`, splitting data, training a Gaussian Naive Bayes classifier, and evaluating its accuracy.

In practice

• Use NLTK for text cleaning and normalization.
• Apply `CountVectorizer` to convert text to numerical features.
• Limit `max_features` in `CountVectorizer` to manage vocabulary size.

Topics

• Sentiment Analysis
• Natural Language Processing
• Naive Bayes Classifier
• Text Preprocessing
• Bag of Words

Best for: AI Engineer, Machine Learning Engineer, AI Student

Related on AIssential

• Clasificación de Sentimientos en Reseñas de Películas con Naive Bayes en Python — Naive Bayes, with proper preprocessing, offers robust and interpretable sentiment classification.
• From Raw Text to Machine Learning: A Complete NLP Pipeline Explained — An NLP pipeline systematically transforms raw text into numerical data for machine learning models through cleaning, preprocessing, and vectorization.
• NLP Looks Scary Until You Understand Bag of Words — Bag of Words represents text numerically by counting word occurrences, ignoring grammar and order.
• On GATE, Text and Social Media Analysis, and Detecting Misinformation Online — A multi-layer analytical pipeline effectively extracts and categorizes digital identity aspects of agrifood products from online discourse.
• From Text to Numbers: Demystifying Text Preprocessing in NLP — NLP text preprocessing, using methods like Bag of Words and TF-IDF, translates human language into numerical data for machine learning models.
• Naïve Bayes: The Algorithm That’s Wrong About Everything But Right About Results — Naïve Bayes effectively classifies by combining prior class probabilities with feature likelihoods, despite a "naïve" independence assumption.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Naturallanguageprocessing on Medium.