nlp-sentiment-pipeline 0.1.2

A comprehensive pipeline for sentiment analysis using deep learning models

NLP Sentiment Analysis Pipeline

A comprehensive, modular pipeline for sentiment analysis using deep learning models. This package provides tools for data extraction, preprocessing, model training, and evaluation.

Features

• Data Preparation: Extract and preprocess text data for sentiment analysis
• Modeling: Baseline models with TF-IDF vectorization and neural networks
• Evaluation: Comprehensive model evaluation utilities

Installation

From PyPI (once published)

pip install nlp-sentiment-pipeline

From Source

git clone https://github.com/FranzCastillo/NLP-Tweets-Sentiment-Analysis-DL-Models
cd NLP-Tweets-Sentiment-Analysis-DL-Models/pipeline
pip install -e .

For Development

pip install -e ".[dev]"

Usage

As a Python Package

from pipeline.data_preparation import DataExtractor, TextPreprocessor, DataSplitter
from pipeline.modeling import BaselineModel, TfidfVectorizerWrapper
from pipeline.evaluation import ModelEvaluator

# Extract data
extractor = DataExtractor(split="train")
df = extractor.extract()

# Preprocess text
preprocessor = TextPreprocessor(remove_stopwords=True)
df['clean_text'] = df['text'].apply(preprocessor.preprocess)

# Train model
model = BaselineModel()
# ... training code ...

# Evaluate
evaluator = ModelEvaluator(model, X_test, y_test)
results = evaluator.evaluate()

As a Command-Line Tool

nlp-sentiment-pipeline

Package Structure

pipeline/
├── __init__.py
├── main.py
├── data_preparation/      # Data extraction and preprocessing
│   ├── __init__.py
│   ├── extraction.py
│   ├── preprocessing.py
│   └── data_splitter.py
├── modeling/              # Model definitions and utilities
│   ├── __init__.py
│   ├── baseline.py
│   ├── vectorizer.py
│   └── model_evaluator.py
└── evaluation/            # Evaluation utilities
    ├── __init__.py
    ├── evaluator.py
    └── model_evaluator.py

Subpackages

data_preparation

Tools for data extraction and preprocessing:

• DataExtractor: Extract datasets from various sources
• TextPreprocessor: Clean and preprocess text data
• DataSplitter: Split data into train/validation/test sets

modeling

Model implementations and utilities:

• BaselineModel: Baseline neural network model
• TfidfVectorizerWrapper: TF-IDF vectorization wrapper
• Various deep learning models

evaluation

Model evaluation tools:

• ModelEvaluator: Comprehensive model evaluation
• evaluate_model: Quick evaluation function
• print_evaluation_results: Pretty-print evaluation metrics

Requirements

• Python >= 3.8
• TensorFlow >= 2.15.0
• pandas >= 2.3.1
• scikit-learn >= 1.5.2
• nltk >= 3.9.1
• spacy >= 3.8.7

See requirements.txt for a complete list of dependencies.

Development

Running Tests

pytest

Code Formatting

black pipeline/

Type Checking

mypy pipeline/

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Authors

Francisco Castillo - [e-mail]

Changelog

0.1.2 (2025-11-15)

• Second test release for PyPI publishing
• CI/CD pipeline testing

0.1.1 (2025-11-15)

• Test release for CI/CD pipeline
• Updated GitHub Actions workflows

0.1.0 (2025-11-14)

• Initial release
• Data preparation subpackage
• Modeling subpackage
• Evaluation subpackage