This APP for Review Negative or Positive Comments Restaurant
This project is to develop a model that can automatically predict positive or negative sentiments or reviews related to a restaurant. The ultimate goal is to help restaurant owners, managers, and stakeholders in the culinary industry understand their customers' feedback more efficiently. With this model, we can identify and categorize customer reviews, allowing restaurant businesses to respond more quickly to customer feedback, improve service, and enhance the food experience offered.
Link Deployment: here
This dataset was created for the Paper 'From Group to Individual Labels using Deep Features', Kotzias et. al,. KDD 2015 contains sentences labeled with a positive or negative sentiment. The score is either 1 (for positive) or 0 (for negative) The sentences come from three different websites/fields: imdb.com, amazon.com, yelp.com. For each website, there exist 500 positive and 500 negative sentences. Those were selected randomly for larger datasets of reviews. We attempted to select sentences that have a clearly positive or negative connotation, the goal was for no neutral sentences to be selected.
https://www.kaggle.com/datasets/marklvl/sentiment-labelled-sentences-data-set https://www.dicoding.com/academies/185/tutorials/10229