https://github.com/singhvishal003/bert-sentiment
Sentiment Analysis Model To Detect the Sentiments.
https://github.com/singhvishal003/bert-sentiment
beautifulsoup numpy pandas requests torch torchvision
Last synced: 11 months ago
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Sentiment Analysis Model To Detect the Sentiments.
- Host: GitHub
- URL: https://github.com/singhvishal003/bert-sentiment
- Owner: Singhvishal003
- Created: 2024-11-22T13:39:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-23T07:21:56.000Z (about 1 year ago)
- Last Synced: 2025-01-28T04:31:20.307Z (about 1 year ago)
- Topics: beautifulsoup, numpy, pandas, requests, torch, torchvision
- Language: Jupyter Notebook
- Homepage:
- Size: 2.93 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Sentiment Analysis Model
### Overview
Sentiment analysis is a natural language processing (NLP) technique used to determine the sentiment expressed in a piece of text. This model classifies text into positive, negative, or neutral categories.
### Key Features
- *Text Classification*: Analyzes text data to identify sentiment.
- *Machine Learning*: Utilizes machine learning algorithms for accurate sentiment prediction.
- *NLP Techniques*: Employs tokenization, stemming, and other NLP methods.
### Applications
- *Social Media Monitoring*: Analyzes tweets, posts, and comments to gauge public opinion.
- *Customer Feedback*: Evaluates reviews and feedback to understand customer satisfaction.
- *Market Research*: Assesses sentiment in news articles and reports for market analysis.
### How It Works
1. *Data Collection*: Gather text data from various sources.
2. *Preprocessing*: Clean and prepare the data (e.g., removing stop words, tokenization).
3. *Feature Extraction*: Convert text into numerical features using techniques like TF-IDF.
4. *Model Training*: Train the model using labeled datasets.
5. *Prediction*: Use the trained model to predict sentiment on new text data.
### Requirements
- Python 3.7+
- Libraries: numpy, pandas, scikit-learn, nltk
### Installation
bash
pip install -r requirements.txt
### Usage
python
from sentiment_model import SentimentAnalyzer
# Initialize the analyzer
analyzer = SentimentAnalyzer()
# Analyze sentiment
text = "I love this product!"
sentiment = analyzer.predict(text)
print(f"Sentiment: {sentiment}")
### Contributing
Contributions are welcome! Please submit a pull request or open an issue for any improvements or bug fixes.