cat🐈: the repo for the paper "Embarrassingly Simple Unsupervised Aspect extraction"
Build a Flask web application to help users retrieve key restaurant information and feature-based reviews (generated by applying market-basket model – Apriori algorithm and NLP on user reviews).
Pencarian Restoran dengan Zomato API
This is the project that I created while working at TCS iON. The model is deployed on Heroku using Flask.
[IEEE'19] [Dataset] Sentiment Analysis of Bengali Texts on Online Restaurant Reviews Using Multinomial Naive Bayes
🍽️ 🌟MWS-Restaurant-Reviews🌟. Nanodegree Mobile Web Specilist.
Ripe Apples - A Restaurant Review Aggregator
Collaboration for my GitHub students (Batch-2) | **Checkout** : https://aungttant.github.io/YourChoice/
Mobile Web Specialist Restaurant Reviews App: Stage 3
Created a fully functional restaurant reviews app with React, allowing users to search for restaurants in their area using Google places API for live data and real time reviews. Users are also able to add new restaurants, add new full review feedback.
Project 6 for FEND Udacity / Convert a static design that lacks accessibility to be responsive on different sized displays and accessible for screen reader use. A service worker is used for creating a seamless offline experience for the users.
Authentic Restaurant Design Website
OpenTable Reviews Classification with BERT and Transfer Learning
Recommendation engine on restaurant reviews
Mobile Web Specialist ND Restaurant Reviews App: Stage 1
Udacity MWS Nanodegree project 3
Restaurant Review Analysis. "Thrilled to unveil my latest project on Streamlit - a cutting-edge restaurant review ML model 🍽️🔍 Using innovative AI technology, share your feedback and predict to model. Join me on LinkedIn to explore the future of culinary exploration! #AI #MachineLearning #Streamlit #RestaurantReviews" .
A repo to query Zomato API
Dependency parsing was used to extract relevant information from a review in order to predict the sentiment of a given aspect term. Different machine learning models such as Naïve Bayes, Logistic Regression, Support Vector Classifier and Neural Networks were used to make predictions. A maximum accuracy score of 0.74 on the test dataset was achie…
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