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PROJECT


Auto-Tagging System for Content Categorization

In this project, we’ll work with various natural language processing techniques, enabling us to generate relevant tags for text data and facilitating classification into different classes.

Auto-Tagging System for Content Categorization

You will learn to:

Write programs in Python with hands-on practice.

Work with different natural language processing techniques.

Handle text data in different ways.

Extract meaningful insights from unstructured data.

Skills

Natural Language Processing

Text Preprocessing

Deep Learning

Data Science

Prerequisites

Intermediate knowledge of Python

Basic knowledge of natural language processing

Familiarity with Python and machine learning libraries

Technologies

spaCy logo

spaCy

Python

Pandas

Project Description

In this project, we’ll get hands-on practice in Python and natural language processing (NLP). We’ll use spaCy, an advanced NLP library in Python, to tackle the challenge of automating content tagging. Our goal is to develop an automated system capable of efficiently tagging textual content. We’ll gain practical experience in text preprocessing, familiarity with spaCy’s robust features, and building a model pipeline that can predict tags accurately.

We’ll primarily utilize spaCy for text preprocessing, entity recognition, and tag generation due to its robust NLP capabilities. For specific text-cleaning tasks, we’ll also take the help of the re library for regular expressions (regex). Additionally, we’ll fine-tune spaCy’s pretrained models with our custom dataset and evaluate the model’s performance using test data, ensuring our tags are accurate and relevant to the content.

Project Tasks

1

Introduction

Task 0: Get Started

Task 1: Import Libraries and Modules

Task 2: Load and Explore the Dataset

2

Data Preprocessing

Task 3: Handle Text Case, Contractions, and URLs

Task 4: Handle Emails and Date Time Elements

Task 5: Remove Numbers and Special Characters

Task 6: Handle Stop Words and Extra Spaces

3

Data Preparation Pipeline

Task 7: Tokenize Cleaned Text

Task 8: Build a Data Preparation Pipeline

Task 9: Create Pattern Matching Flow

4

Tag Prediction

Task 10: Entity Extraction Using spaCy Model

Task 11: Optimizing the spaCy Model

5

Tagging Automation

Task 12: Optimizing Entity Extraction for Auto-Tagging

Task 13: Enhancing Entity Aggregation for Workflow Optimization

Task 14: Preparing and Refining Test Data for Entity Analysis

Task 15: Compute the Evaluation Metrics

Congratulations!