sharmaroshan/Restaurant-Reviews-Analysis
Using Natural Language Processing and Bag of Words for feature extraction for sentiment analysis of the customers visited in the Restaurant and at last using Classification algorithm to separate Positive and Negative Sentiments.
The pipeline implements text preprocessing with stemming and stopword removal before vectorization, then trains multiple classification algorithms (likely including Naive Bayes or SVM) on labeled restaurant review datasets. The project uses scikit-learn for machine learning workflows and pandas for data manipulation, enabling comparative model evaluation to identify the best-performing classifier for binary sentiment prediction on raw customer review text.
No commits in the last 6 months.
Stars
12
Forks
3
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Mar 31, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/sharmaroshan/Restaurant-Reviews-Analysis"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
prateekmaj21/Restaurant-Reviews-NLP-Project
A Natural Language Processing project based on Restaurant reviews.
GeorgiosEtsias/Kmeans-Clustering-NLP-RestaurantOrders
The project categorizes 160K food orders from 100 different pizza shops. Multiple iterations of...
apoorvrajdev/restaurant-sentiment-analysis
NLP + Machine Learning project that classifies restaurant reviews as positive or negative using...
chenzhivis/Analysis-and-Classification-of-Restaurant-Reviews
Developed a NLP classification model that can classify negative reviews of restaurants, help...
travisdhuang/Epicurious_Recipes
Machine Learning Study on a Dataset of Epicurious Recipes