haoopeng/CNN-yelp-challenge-2016-sentiment-classification
IPython Notebook for training a word-level Convolutional Neural Network model for sentiment classification task on Yelp-Challenge-2016 review dataset.
This project helps businesses understand customer sentiment from written reviews. It takes raw customer feedback text and converts it into a sentiment rating, either positive/negative or a 1-5 star prediction. This is useful for market researchers or customer experience managers who need to quickly gauge how customers feel about products or services.
112 stars. No commits in the last 6 months.
Use this if you have a large volume of customer review text and need an automated way to classify sentiment or predict star ratings.
Not ideal if you need extremely high accuracy for multi-star prediction, as the model achieved approximately 40% accuracy in that scenario.
Stars
112
Forks
48
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 02, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/haoopeng/CNN-yelp-challenge-2016-sentiment-classification"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
nas5w/imdb-data
A JSON file of 50,000 IMDB movie reviews to be used in machine learning applications.
RubixML/Sentiment
An example project using a feed-forward neural network for text sentiment classification trained...
VuBacktracking/mamba-text-classification
Text Classification using Mamba Model
cmasch/cnn-text-classification
Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0
Taha533/Sentiment-Analysis-of-IMDB-Movie-Reviews
This project focuses on sentiment analysis of movie reviews using the IMDb dataset. The dataset...