{"id":24738116,"url":"https://github.com/ryancodingg/rotten-tomatoes-classification-model","last_synced_at":"2026-04-29T11:04:54.303Z","repository":{"id":178212489,"uuid":"655950903","full_name":"ryancodingg/Rotten-tomatoes-classification-model","owner":"ryancodingg","description":"The motivation behind this project is to build a machine learning model that can predict the Rotten Tomatoes rating of movies based on critic reviews. 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By utilizing two large datasets, namely rotten_tomatoes_critic_reviews.csv and rotten_tomatoes_movies.csv obtained from Kaggle, I aim to develop a classification algorithm that can effectively predict the rating of movies.\n\nRotten Tomatoes is a popular online platform that provides movie reviews and ratings. The ratings provided on Rotten Tomatoes are determined by a combination of critics' reviews and audience ratings. Critics' reviews play a significant role in influencing the overall rating assigned to a movie. Therefore, by analyzing critic reviews, we can gain insights into the sentiment and opinions expressed by critics, which can subsequently be used to predict the movie's Rotten Tomatoes rating.\n\n# Dataset Description\n\nI use 2 dataset: \n*  `rotten_tomatoes_critic_reviews.csv`\n    - `rotten_tomatoes_link`: each link will associate with an unique ID of a movie\n    - `critic_name`: name of critic\n    - `top_critic`: tomatometer-approved critic: True or False\n    - `publisher_name`: name of Publisher\n    - `review_type`: Fresh, Rotten or Certified Fresh \n    - `review_score`: score for the movie\n    - `review_date`: date of review\n    - `review_content`: content of the review\n* `rotten_tomatoes_movies.csv`\n    - `rotten_tomatoes_link`: each link will associate with an unique ID of a movie\n    - `movie_title`: name of the movie\n    - `movie_info`: brief introducrion of the movie\n    - `critic_consensus`: rotten Tomatoes's comment\n    - `content_rating`: rating of content - PG, R, NR, PG-13,G \n    - `genres`: type of movie\n    - `directors`: name of directors\n    - `authors`: name of authors\n    - `actors`: list of actors\n    - `original_release_date`: date which first made available to public\n    - `streaming_release_date`: for only streaming providers\n    - `runtime`: length of movie\n    - `production_company`: name of production house\n    - `tomatometer_status`: Fresh, Rotten or Certified Fresh\n    - `tomatometer_rating`: percentage of positive reviews\n    - `tomatometer_count`: critic ratings counted for the calculation of the tomatomer status\n    - `audience_status`: label assigned Spill or Upright\n    - `audience_rating`: percentage of positive rating users\n    - `audience_count`: total rating audience\n    - `tomatometer_top_critics_count`: number of rating by top critics\n    - `tomatometer_fresh_critics_count`:  number of critic ratings labeled \"Fresh\"\n    - `tomatometer_rotten_critics_count`: number of critic ratings labeled \"Rotten\" \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fryancodingg%2Frotten-tomatoes-classification-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fryancodingg%2Frotten-tomatoes-classification-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fryancodingg%2Frotten-tomatoes-classification-model/lists"}