https://github.com/sayande01/amazon_review_sentiment_analysis
This project is a tutorial on sentiment analysis in Python, using two approaches: VADER and the transformer-based RoBERTa model from Hugging Face. The Jupyter Notebook guides you through data preprocessing, model implementation, and result interpretation, showcasing traditional and advanced NLP techniques for sentiment analysis.
https://github.com/sayande01/amazon_review_sentiment_analysis
nltk roberta-model transformer vader-sentiment-analysis
Last synced: 3 months ago
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This project is a tutorial on sentiment analysis in Python, using two approaches: VADER and the transformer-based RoBERTa model from Hugging Face. The Jupyter Notebook guides you through data preprocessing, model implementation, and result interpretation, showcasing traditional and advanced NLP techniques for sentiment analysis.
- Host: GitHub
- URL: https://github.com/sayande01/amazon_review_sentiment_analysis
- Owner: sayande01
- Created: 2024-11-11T14:56:47.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-11-11T15:00:56.000Z (8 months ago)
- Last Synced: 2025-02-13T02:38:39.293Z (4 months ago)
- Topics: nltk, roberta-model, transformer, vader-sentiment-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 896 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
**Title:** Sentiment Analysis in Python: Comprehensive Guide Using VADER and RoBERTa Models
**Description:**
This project is a complete tutorial on sentiment analysis in Python, where we leverage two popular approaches: the VADER sentiment analysis tool and the transformer-based RoBERTa model from Hugging Face. This repository includes a detailed Jupyter Notebook that walks through each step of the analysis, from data preprocessing and model implementation to interpreting the results. Follow along to gain a solid understanding of how traditional and advanced NLP techniques can be applied to analyze sentiment in text data.
In this notebook, we will cover:
1. **VADER Sentiment Analysis:** Using a rule-based, bag-of-words approach, VADER (Valence Aware Dictionary and sEntiment Reasoner) scores sentiments based on words with pre-defined intensities. This method is particularly effective for social media and short-text data where lexicon-based sentiment analysis provides reliable results.
2. **RoBERTa Model via Hugging Face:** Employing a pretrained RoBERTa model, a robust transformer-based architecture, we delve into a deep learning approach that captures context and nuance in textual data. This model, accessed through the Hugging Face pipeline, allows for more accurate sentiment analysis, especially for complex sentences.
**Objective:**
The primary goal of this project is to provide a hands-on guide for building and understanding two different approaches to sentiment analysis, suitable for various applications like social media monitoring, product reviews, and customer feedback analysis. Specifically, we aim to:
- **Demonstrate VADER's Functionality:** Show how to use the VADER tool for quick and effective sentiment scoring in scenarios where interpretability and speed are prioritized.
- **Explore Transformer-Based Sentiment Analysis with RoBERTa:** Illustrate how to leverage Hugging Face’s API to run RoBERTa, achieving a deeper and more contextual understanding of sentiment in text data.
- **Compare Results:** Analyze the differences in sentiment results obtained through VADER and RoBERTa, helping users understand when to use each method based on the complexity and type of text data.
This project is designed to help both beginners and intermediate NLP practitioners understand the strengths and limitations of traditional and advanced sentiment analysis methods, equipping them with tools to apply sentiment analysis effectively in real-world projects.