Twitter Text Preprocessing & Prediction
This project focuses on preprocessing Twitter text data and building prediction models for text classification or sentiment analysis. The project includes comprehensive text preprocessing pipelines and machine learning models for Twitter data analysis. The work is divided into preprocessing notebooks and prediction notebooks, with a text-only version focusing on textual features. It demonstrates Twitter-specific text cleaning, feature engineering, and model evaluation for NLP tasks.
Overview
This project focuses on preprocessing Twitter text data and building prediction models for text classification or sentiment analysis. The project includes comprehensive text preprocessing pipelines and machine learning models for Twitter data analysis. The work is divided into preprocessing notebooks and prediction notebooks, with a text-only version focusing on textual features. It demonstrates Twitter-specific text cleaning, feature engineering, and model evaluation for NLP tasks.
Key Features
Twitter-specific text preprocessing (mentions, hashtags, URLs)
Text normalization and cleaning
Tokenization and stop word removal
Stemming or lemmatization
TF-IDF and Count Vectorizer for feature extraction
N-gram features (unigrams, bigrams, trigrams)
Statistical feature extraction (text length, word count)
Multiple classification models
Comprehensive evaluation metrics
Text-only prediction pipeline
pages.portfolio.projects.twitter_text_preprocessing_prediction.features.10
Technical Highlights
Implemented Twitter-specific text preprocessing pipeline
Created comprehensive feature extraction with TF-IDF and n-grams
Built multiple classification models for text prediction
Handled Twitter-specific elements (mentions, hashtags, URLs, emojis)
Demonstrated text normalization and cleaning workflows
Evaluated models with comprehensive metrics
Challenges and Solutions
Twitter-Specific Text Formatting
Created custom preprocessing functions to handle mentions, hashtags, URLs, and special formatting
Text Noise and Variability
Implemented robust normalization and cleaning pipelines for informal language, typos, and slang
Feature Extraction from Text
Used TF-IDF vectorization and statistical feature extraction to convert unstructured text to numerical features
High Dimensionality
Applied feature selection, dimensionality reduction, and sparse representations for large vocabularies
Class Imbalance
Used stratified sampling, class weights, and resampling techniques to handle uneven class distributions
Emojis and Special Characters
Implemented emoji normalization and Unicode handling for special characters
Technologies
NLP
ML Models
Vectorization
Preprocessing
Data
Environment
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- NLP Pipeline Workflow with Text Preprocessing and Feature Extraction
- Category
- Data Science