MATLAB Text Analytics: Slang Classifier App

A practical natural language processing (NLP) application built using MATLAB that classifies slang or informal text into meaningful categories using machine learning and text analytics techniques.

Overview

This project demonstrates how unstructured text data—such as slang, abbreviations, or informal language—can be processed, analyzed, and classified into structured insights using MATLAB.

The application is implemented as an interactive app, making it accessible for both technical and non-technical users.

Think of this like a “translator for informal language”—it takes messy, human-written text and turns it into something structured and understandable for analysis or decision-making.

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Objective

• Convert raw text (slang/informal language) into structured features
• Train a machine learning model to classify text into categories
• Provide an interactive interface for users to input and classify text
• Demonstrate an end-to-end NLP workflow within MATLAB

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Project Workflow

This project follows a structured NLP pipeline:

1. Text Data Collection

• Input dataset containing slang or informal text
• Define classification labels/categories

2. Text Preprocessing

• Tokenization (breaking sentences into words)
• Lowercasing and normalization
• Removal of stopwords (e.g., "the", "is")
• Handling slang variations and noise

3. Feature Extraction

• Convert text into numerical representation using:
  • Bag-of-Words
  • Term Frequency / TF-IDF (if implemented)

4. Model Training

• Train a classification model using MATLAB Text Analytics capabilities
• Example models:
  • Naive Bayes
  • Logistic Regression
  • Support Vector Machine (depending on implementation)

5. Model Evaluation

• Validate model accuracy
• Analyze classification performance

6. App Deployment

• Build an interactive app using MATLAB App Designer
• Allow users to:
  • Input custom text
  • Run classification
  • View prediction results instantly

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Key Concepts Demonstrated

• Natural Language Processing (NLP)
• Text preprocessing and cleaning
• Feature engineering for text data
• Supervised machine learning classification
• Model evaluation and validation
• Interactive app development using MATLAB

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Tech Stack

• MATLAB
• Text Analytics Toolbox
• Statistics and Machine Learning Toolbox
• App Designer

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Project Structure

MATLAB-TextAnalytics-SlangClassifier-App/
├── images/                      # App Designer image files
├── models/                      # App Designer model files 
├── trainingScripts/rawTexts     # Dataset (if included)
├── trainingScripts/             # Preprocessing / training scripts
├── WordClassifier.mlapp         # MATLAB App Designer
├── SlangClassifier.prj          # MATLAB Project file
├── StandaloneDesktopApp/        # Final output application
└── README.md