https://github.com/sathviknayak123/sentiment-anyalysis
Sentiment Analysis using DistlBERT Transformer from HuggingFace. Also integrated Airflow for end-to-end pipeline
https://github.com/sathviknayak123/sentiment-anyalysis
airflow astronomer distilbert flask huggingface-transformer nlp python s3-bucket selenium-webdriver sentiment-analysis tensorflow web-scraping-python
Last synced: 7 months ago
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Sentiment Analysis using DistlBERT Transformer from HuggingFace. Also integrated Airflow for end-to-end pipeline
- Host: GitHub
- URL: https://github.com/sathviknayak123/sentiment-anyalysis
- Owner: SathvikNayak123
- License: mit
- Created: 2024-09-24T13:10:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-26T09:11:30.000Z (10 months ago)
- Last Synced: 2025-02-13T02:47:45.469Z (9 months ago)
- Topics: airflow, astronomer, distilbert, flask, huggingface-transformer, nlp, python, s3-bucket, selenium-webdriver, sentiment-analysis, tensorflow, web-scraping-python
- Language: Jupyter Notebook
- Homepage:
- Size: 17.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Sentiment Analysis with DistilBERT
## Project Overview
This project focuses on building a sentiment analysis model to predict the sentiment of customer reviews using **DistilBERT**. The pipeline involves scraping reviews, preprocessing the data, training a fine-tuned DistilBERT model, and deploying it through a Flask application. The workflow is automated using **Apache Airflow**.
---
## What is DistilBERT?
- DistilBERT is a smaller, faster, and more efficient version of BERT. It was developed through a process called knowledge distillation, where a smaller model (the student) is trained to reproduce the behavior of a larger model (the teacher) while retaining most of its performance.
- DistilBERT retains 97% of BERT's performance on various NLP tasks while being 40% smaller and 60% faster.
- BERT consists of 12 transformer layers, an embedding layer and a prediction layer.
- During distillation, DistilBERT learns from BERT by mimicking its outputs (logits) and intermediate representations.
- As a resilt, DistilBERT has 6 transformer layers (half of BERT's 12 layers) while maintaining similar functionality.
---
## Features
- **Data Collection**:
- Scraped 20,000+ Amazon reviews using **Selenium WebDriver**.
- Stored the collected data securely in an **AWS S3 bucket**.
- **Data Preprocessing**:
- Cleaned and processed reviews and ratings:
- Removed non-English Reviews
- Removed stopwords, special characters, and extra spaces.
- Performed lemmatization and stemming to normalize text.
- Encoded ratings ranging from 0-5 stars to labels negative(0), neutral(1) and positive(2)
- Stored the processed data in **S3** for further use.
- **Data Tokenization and Preparation**:
- Tokenized reviews using **DistilBERT tokenizer**.
- Divided the dataset into **training**, **validation**, and **testing** sets.
- **Model Training**:
- Imported **DistilBERT** from **Hugging Face Transformers**.
- Fine-tuned the model on the dataset.
- Implemented **early stopping** to optimize training.
- Used **customized class weights** to handle class imbalance in training dataset.
- **Deployment**:
- Built a user-friendly **Flask application** for making predictions.
- **Workflow Automation**:
- Integrated **Apache Airflow** to automate the entire pipeline.
---
## Result
- **Accuracy**: Achieved an accuracy of 85% on the test dataset.
- **F1-score**: Achieved f1-scores of 0.9 for positive class and 0.7 for others on the test dataset.
- Enhanced multi-class classification performance by implementing class-weighted training, improving the Precision-Recall AUC for minority classes (e.g., 'neutral') by over 30%, resulting in more balanced and accurate predictions across all categories
- Before:
- After class-weighted training:
## Getting Started
To get started with this project, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/SathvikNayak123/sentiment-anyalysis
```
2. Install the necessary dependencies:
```bash
pip install -r requirements.txt
```
3. Add AWS S3 credentials in .env file
4. Run Airflow to execute scraping and training pipeline:
```bash
astro dev init
astro dev start
5. Run app for prediction
```bash
python app.py
```