https://github.com/elmezianech/classifyreviews_nlp

Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.
https://github.com/elmezianech/classifyreviews_nlp

accuracy-score ai countvectorizer cross-validation joblib logistic-regression machine-learning ml multinomialnb naive-bayes-classifier nltk-library numpy pandas randomforestclassifier svc

Last synced: about 1 month ago
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Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.

• Host: GitHub
• URL: https://github.com/elmezianech/classifyreviews_nlp
• Owner: elmezianech
• Created: 2023-11-14T10:35:12.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-14T10:50:39.000Z (about 1 year ago)
• Last Synced: 2023-11-15T11:40:43.749Z (about 1 year ago)
• Topics: accuracy-score, ai, countvectorizer, cross-validation, joblib, logistic-regression, machine-learning, ml, multinomialnb, naive-bayes-classifier, nltk-library, numpy, pandas, randomforestclassifier, svc
• Language: Jupyter Notebook
• Homepage:
• Size: 7.81 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Insights-Analysis-NLP

This NLP Insights Analyzer project leverages a combination of text preprocessing techniques, including stemming, stop-word removal, and CountVectorizer, to transform raw customer reviews into a format suitable for machine learning models. The project explores various classification models, including Logistic Regression, Naive Bayes, Support Vector Machines, and Random Forests, to predict and categorize sentiments.

The models are evaluated using cross-validation to ensure robust performance across different datasets. The best-performing model is then saved and can be easily loaded for making predictions on new, unseen data.

This project used the "ClassifyReviews_NLP/Restaurant_Reviews.tsv" sourced from Github. The dataset contains a collection of reviews labeled as positive and negative for training and testing the classifier.

Link: https://github.com/PritiG1/ClassifyReviews_NLP/blob/main/Restaurant_Reviews.tsv