https://github.com/yvandana/sentiment-analysis-on-amazon-reviews
Performing Sentiment Analysis on Amazon Reviews using VADER & Roberta via Hugging Face
https://github.com/yvandana/sentiment-analysis-on-amazon-reviews
nltk-python roberta-sentiment-analysis sentiment-analysis vader-sentiment-analysis
Last synced: 4 months ago
JSON representation
Performing Sentiment Analysis on Amazon Reviews using VADER & Roberta via Hugging Face
- Host: GitHub
- URL: https://github.com/yvandana/sentiment-analysis-on-amazon-reviews
- Owner: YVandana
- License: gpl-3.0
- Created: 2024-12-27T00:54:28.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-12-27T01:10:46.000Z (6 months ago)
- Last Synced: 2025-01-03T03:19:43.440Z (6 months ago)
- Topics: nltk-python, roberta-sentiment-analysis, sentiment-analysis, vader-sentiment-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 1.82 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Sentiment Analysis on Amazon Reviews
## Overview
This project performs sentiment analysis on Amazon product reviews using two different methodologies: VADER (Valence Aware Dictionary and sEntiment Reasoner) and a pre-trained RoBERTa model from Hugging Face. The analysis aims to classify the sentiment of reviews into positive, negative, or neutral categories.
## Table of Contents
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Data Overview](#data-overview)
- [Methodology](#methodology)
- [Using VADER](#using-vader)
- [Using RoBERTa](#using-roberta)
- [Creating a Transformer Pipeline](#creating-a-transformer-pipeline)
- [Results](#results)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## Prerequisites
Before running the project, ensure you have the following installed:
- Python 3.7 or higher
- pip
- Jupyter Notebook or Google Colab
## Installation
To install the required packages, run the following commands:
```bash
pip install pandas numpy matplotlib seaborn nltk transformers
```
## Data Overview
The dataset used in this project is Reviews.csv, which contains Amazon product reviews. The initial dataset has 568,454 entries, but for analysis, we utilize the first 500 entries.
## Data Structure
The dataset contains the following columns:
Id: Unique identifier for each review
ProductId: Identifier for the product
UserId: Identifier for the user
ProfileName: Name of the user
HelpfulnessNumerator: Number of users who found the review helpful
HelpfulnessDenominator: Total number of users who rated the review
Score: Rating given by the user (1 to 5 stars)
Time: Timestamp of the review
Summary: Summary of the review
Text: Full text of the review
## Methodology
### Using VADER
1. Import Libraries:
```
import pandas as pd
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
from tqdm.notebook import tqdm
```
2. Load Data
```
df = pd.read_csv('Reviews.csv')
df = df.head(500) # Use the first 500 entries for analysis
```
3. Performing Sentiment Analysis
Initilaising VADER and computing Polarity Scores
```
sia = SentimentIntensityAnalyzer()
res = {}
for i, row in tqdm(df.iterrows(), total=len(df)):
text = row['Text']
myid = row['Id']
res[myid] = sia.polarity_scores(text)
vaders = pd.DataFrame(res).T.reset_index().rename(columns={'index': 'Id'})
```
### Using ROBERTA
1. Importing Libraries
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from scipy.special import softmax
```
2. Load Model
```
MODEL = "cardiffnlp/twitter-roberta-base-sentiment"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
```
3. Compute Sentiment Scores
```
def polarity_scores_roberta(example):
encoded_text = tokenizer(example, return_tensors='pt')
output = model(**encoded_text)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
return {
'roberta_neg': scores[0],
'roberta_neu': scores[1],
'roberta_pos': scores[2]
}
```
4. Run Sentiment Analysis:
Iterate through the DataFrame and compute sentiments using RoBERTa.
### Creating a Transformer Pipeline
For ease of use, a sentiment analysis pipeline can be created:
```
sent_pipeline = pipeline("sentiment-analysis")
```
## Results
The results of the sentiment analysis from both VADER and RoBERTa can be visualized using plots. The analysis provides insight into the distribution of sentiments across different star ratings.
## Usage
Clone the repository or download the project files.
Open a terminal and navigate to the project directory.
Launch the Jupyter Notebook or Google Colab.
Run the cells sequentially to perform sentiment analysis.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
## License
This project is licensed under the MIT License. See the LICENSE file for more details.
## Acknowledgments
VADER for the sentiment analysis tool.
Hugging Face for providing powerful transformer models.
Pandas and Matplotlib for data manipulation and visualization.