NLP Sentiment Analysis Project

Project Overview

This project builds a text classification pipeline for emotion detection using NLP techniques. The workflow includes data loading, preprocessing, feature extraction, model training, and evaluation.

What Was Done

• Loaded the dataset from train.txt.
• Cleaned and preprocessed text data.
• Converted emotion labels to numeric values.
• Vectorized text using Bag-of-Words and TF-IDF.
• Trained and evaluated multiple classification models.

Data Files

• train.txt: Training dataset containing text and emotion labels.
• test.txt: Optional test data file for further evaluation.
• val.txt: Optional validation data file for model tuning.

Preprocessing Steps

• Lowercased all text.
• Removed URLs.
• Removed digits.
• Removed emojis.
• Removed punctuation.
• Removed stop words.

Models Evaluated

• Multinomial Naive Bayes
• Logistic Regression
• Support Vector Machine (SVM)

Results

The models were evaluated using:

• Accuracy
• Precision
• Recall

Summary Table

Step	Description
Data Loading	Read train.txt with text and emotion labels.
Label Encoding	Mapped emotion labels to integer values.
Text Cleaning	Lowercase, remove URLs, digits, emojis, punctuation, and stop words.
Vectorization	Converted text into numeric features using Bag-of-Words and TF-IDF.
Model Training	Trained Naive Bayes, Logistic Regression, and SVM models.
Evaluation	Compared models using accuracy, precision, and recall.

Notes

This project focuses on text preprocessing and classification for emotion detection. The notebook includes the full pipeline for preparing the data and evaluating models.