https://github.com/sanjaikumar-28/sentimental-analysis
This repository explores the world of restaurant reviews, using Support Vector Machines (SVM) and CountVectorizer to predict the sentiment (positive or negative) expressed in each review. By analyzing textual data, we aim to provide valuable insights for restaurants and improve the overall customer experience.
https://github.com/sanjaikumar-28/sentimental-analysis
countvectorizer jupyter-notebook machine-learning pipeline python sentiment-analysis support-vector-machines
Last synced: 9 months ago
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This repository explores the world of restaurant reviews, using Support Vector Machines (SVM) and CountVectorizer to predict the sentiment (positive or negative) expressed in each review. By analyzing textual data, we aim to provide valuable insights for restaurants and improve the overall customer experience.
- Host: GitHub
- URL: https://github.com/sanjaikumar-28/sentimental-analysis
- Owner: SANJAIKUMAR-28
- Created: 2023-05-30T08:04:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-02-04T10:44:57.000Z (about 2 years ago)
- Last Synced: 2025-05-07T19:11:39.630Z (9 months ago)
- Topics: countvectorizer, jupyter-notebook, machine-learning, pipeline, python, sentiment-analysis, support-vector-machines
- Language: Jupyter Notebook
- Homepage:
- Size: 14.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Predicting Restaurant Review Sentiment with SVM: Unveiling Customer Opinions
This repository explores the world of restaurant reviews, using Support Vector Machines (SVM) and CountVectorizer to predict the sentiment (positive or negative) expressed in each review. By analyzing textual data, we aim to provide valuable insights for restaurants and improve the overall customer experience.
Key Features:
Explore restaurant reviews: Immerse yourself in the dataset, analyzing real customer opinions and feedback.
Implement CountVectorizer: Transform textual reviews into numerical features suitable for machine learning.
Train and evaluate SVM model: Build a robust SVM model to categorize reviews as positive or negative based on word usage.
Visualize model performance: Assess the model's accuracy and effectiveness through confusion matrices and other metrics.
Analyze key terms: Identify words and phrases most indicative of positive and negative sentiment.
Test on new reviews: Use the trained model to predict sentiment for unseen reviews and provide real-time feedback.