Ratings & Reviews Datasets


The Movies Dataset Metadata on over 45,000 movies. 100,000 ratings for 9,000 movies from 700 users.
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Context These files contain metadata for all 45,000 movies listed in the Full MovieLens Dataset. The dataset consists of movies released on or before July 2017. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. This dataset also has files containing 100,000 ratings from 700 users for a small subset of 9,000 movies. Ratings are on a scale of 1-5 and have been obtained from the official GroupLens website. Content This dataset consists of the following files: movies_metadata.csv: The main Movies Metadata file. Contains information on 45,000 movies featured in the Full MovieLens dataset. Features include posters, backdrops, budget, revenue, release dates, languages, production countries and companies. keywords.csv: Contains the movie plot keywords for our MovieLens movies. Available in the form of a stringified JSON Object. credits.csv: Consists of Cast and Crew Information for all our movies. Available in the form of a stringified JSON Object. links.csv: The file that contains the TMDB and IMDB IDs of all the movies featured in the Full MovieLens dataset. links_small.csv: Contains the TMDB and IMDB IDs of a small subset of 9,000 movies of the Full Dataset. ratings_small.csv: The subset of 100,000 ratings from 700 users on 9,000 movies.

Category: Ratings & Reviews

Keywords: data,analysis,popular,culture,film

Rows: 291741

Sales: 0

Questions: 0

Restaurants on Yellowpages.com
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Context This is a pre-crawled dataset, taken as subset of a bigger dataset (more than 85,000 restaurants) that was created by extracting data from yellowpages.com.   Content This dataset has following fields: Url Name Street Zip Code City State Phone Email Website Categories - A comma-delimited (,) list of categories the listing in question falls under. Most listings are placed in multiple categories. Acknowledgements This dataset was created by PromptCloud's in-house web-crawling service. Inspiration Analyses of city and categories can be performed.   CREDIT: PromptCloud at kaggle

Category: Ratings & Reviews

Keywords: yellowpages,restaurants

Rows: 6002

Sales: 0

Questions: 0

IMDB data from 2006 to 2016
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Here's a data set of 1,000 most popular movies on IMDB in the last 10 years. The data points included are: Title, Genre, Description, Director, Actors, Year, Runtime, Rating, Votes, Revenue, Metascrore Feel free to tinker with it and derive interesting insights.   CREDIT: Prompt Cloud at kaggle

Category: Ratings & Reviews

Keywords: IMDB,entertainment,movies

Rows: 851

Sales: 0

Questions: 0

Restaurants on TripAdvisor
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Context This is a pre-crawled dataset, taken as subset of a bigger dataset (more than 14,92,992 restaurants) that was created by extracting data from TripAdvisor.com. Content This dataset has following fields: Restaurant URL Name Address Phone City State Country Neighbourhood Email ID Menu Website Latitude Longitude About Restaurant Cuisine Good for(suitable) Price Currency Rating Ranking Deal(Promotion) Total Review Last Reviewed Recommended Dining Option Award Acknowledgements This dataset was created by PromptCloud's in-house web-crawling service. Inspiration The country-wise analyses of cuisine, rating, ranking, etc. can be performed.   CREDIT: Prompt Cloud at kaggle

Category: Ratings & Reviews

Keywords: restaurants,tripadvisor,food,entertainment

Rows: 17851

Sales: 0

Questions: 0

Restaurant Data with Consumer Ratings
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Context This dataset was used for a study where the task was to generate a top-n list of restaurants according to the consumer preferences and finding the significant features. Two approaches were tested: a collaborative filter technique and a contextual approach: (i) The collaborative filter technique used only one file i.e., rating_final.csv that comprises the user, item and rating attributes. (ii) The contextual approach generated the recommendations using the remaining eight data files. Content There are 9 data files and a README, and are grouped like this: Restaurants 1 chefmozaccepts.csv 2 chefmozcuisine.csv 3 chefmozhours4.csv 4 chefmozparking.csv 5 geoplaces2.csv Consumers 6 usercuisine.csv 7 userpayment.csv 8 userprofile.csv User-Item-Rating 9 rating_final.csv More detailed file descriptions can also be found in the README: 1 chefmozaccepts.csv Instances: 1314 Attributes: 2 placeID: Nominal Rpayment: Nominal, 12 2 chefmozcuisine.csv Instances: 916 Attributes: 2 placeID: Nominal Rcuisine: Nominal, 59 3 chefmozhours4.csv Instances: 2339 Attributes: 3 placeID: Nominal hours: Nominal, Range:00:00-23:30 days: Nominal, 7 4 chefmozparking.csv Instances: 702 Attributes: 2 placeID: Nominal parking_lot: Nominal, 7 5 geoplaces2.csv Instances: 130 Attributes: 21 placeID: Nominal latitude: Numeric longitude: Numeric the_geom_meter: Nominal (Geospatial) name: Nominal address: Nominal,Missing: 27 city: Nominal, Missing: 18 state: Nominal, Missing: 18 country: Nominal, Missing: 28 fax: Numeric, Missing: 130 zip: Nominal,Missing: 74 alcohol: Nominal, Values: 3 smoking_area: Nominal, 5 dress_code: Nominal, 3 accessibility: Nominal, 3 price: Nominal, 3 url: Nominal, Missing: 116 Rambience: Nominal, 2 franchise: Nominal, 2 area: Nominal, 2 other_services: Nominal, 3 6 rating_final.csv Instances: 1161 Attributes: 5 userID: Nominal placeID: Nominal rating: Numeric, 3 food_rating: Numeric, 3 service_rating: Numeric, 3 7 usercuisine.csv Instances: 330 Attributes: 2 userID: Nominal Rcuisine: Nominal, 103 8 userpayment.csv Instances: 177 Attributes: 2 userID: Nominal Upayment: Nominal, 5 9 userprofile Instances: 138 Attributes: 19 userID: Nominal latitude: Numeric longitude: Numeric the_geom_meter: Nominal (Geospatial) smoker: Nominal drink_level: Nominal, 3 dress_preference:Nominal, 4 ambience: Nominal, 3 transport: Nominal, 3 marital_status: Nominal, 3 hijos: Nominal, 3 birth_year: Nominal interest: Nominal, 5 personality: Nominal, 4 religion: Nominal, 5 activity: Nominal, 4 color: Nominal, 8 weight: Numeric budget: Nominal, 3 height: Numeric Acknowledgements This dataset was originally downloaded from the UCI ML Repository: UCI ML Creators: Rafael Ponce Medellín and Juan Gabriel González Serna rafaponce@cenidet.edu.mx, gabriel@cenidet.edu.mx Department of Computer Science. National Center for Research and Technological Development CENIDET, México Donors of database: Blanca Vargas-Govea and Juan Gabriel González Serna blanca.vargas@cenidet.edu.mx/blanca.vg@gmail.com, gabriel@cenidet.edu.mx Department of Computer Science. National Center for Research and Technological Development CENIDET, México Inspiration Use this data to create a restaurant recommender or determine which restaurants a person is most likely to visit.   Credit: UCI Machine Learning at kaggle

Category: Ratings & Reviews

Keywords: restaurants,ratings,reviews,business

Rows: 6921

Sales: 0

Questions: 0

The Movies Dataset
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Context These files contain metadata for all 45,000 movies listed in the Full MovieLens Dataset. The dataset consists of movies released on or before July 2017. Data points include cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. This dataset also has files containing 100,000 ratings from 700 users for a small subset of 9,000 movies. Ratings are on a scale of 1-5 and have been obtained from the official GroupLens website. Content This dataset consists of the following files: movies_metadata.csv: The main Movies Metadata file. Contains information on 45,000 movies featured in the Full MovieLens dataset. Features include posters, backdrops, budget, revenue, release dates, languages, production countries and companies. keywords.csv: Contains the movie plot keywords for our MovieLens movies. Available in the form of a stringified JSON Object. credits.csv: Consists of Cast and Crew Information for all our movies. Available in the form of a stringified JSON Object. links.csv: The file that contains the TMDB and IMDB IDs of all the movies featured in the Full MovieLens dataset. links_small.csv: Contains the TMDB and IMDB IDs of a small subset of 9,000 movies of the Full Dataset. ratings_small.csv: The subset of 100,000 ratings from 700 users on 9,000 movies. The Full MovieLens Dataset consisting of 26 million ratings and 750,000 tag applications from 270,000 users on all the 45,000 movies in this dataset can be accessed here Acknowledgements This dataset is an ensemble of data collected from TMDB and GroupLens. The Movie Details, Credits and Keywords have been collected from the TMDB Open API. This product uses the TMDb API but is not endorsed or certified by TMDb. Their API also provides access to data on many additional movies, actors and actresses, crew members, and TV shows. You can try it for yourself here. The Movie Links and Ratings have been obtained from the Official GroupLens website. The files are a part of the dataset available here Inspiration This dataset was assembled as part of my second Capstone Project for Springboard's Data Science Career Track. I wanted to perform an extensive EDA on Movie Data to narrate the history and the story of Cinema and use this metadata in combination with MovieLens ratings to build various types of Recommender Systems. Both my notebooks are available as kernels with this dataset: The Story of Film and Movie Recommender Systems Some of the things you can do with this dataset: Predicting movie revenue and/or movie success based on a certain metric. What movies tend to get higher vote counts and vote averages on TMDB? Building Content Based and Collaborative Filtering Based Recommendation Engines.   Credit : Rounak Banik  on Kaggle

Category: Ratings & Reviews

Keywords: Movie,entertainment,Review,rating,Keyword

Rows: 146710

Sales: 0

Questions: 0

Wine Reviews
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The data consists of 10 fields: Points: the number of points WineEnthusiast rated the wine on a scale of 1-100 (though they say they only post reviews for wines that score >=80) Variety: the type of grapes used to make the wine (ie Pinot Noir) Description: a few sentences from a sommelier describing the wine's taste, smell, look, feel, etc. Country: the country that the wine is from Province: the province or state that the wine is from Region 1: the wine growing area in a province or state (ie Napa) Region 2: sometimes there are more specific regions specified within a wine growing area (ie Rutherford inside the Napa Valley), but this value can sometimes be blank Winery: the winery that made the wine Designation: the vineyard within the winery where the grapes that made the wine are from Price: the cost for a bottle of the wine Data scraped from WineEnthusiast during the week of June 15th, 2017. Published by Kaggle user zackthoutt https://www.kaggle.com/zynicide

Category: Ratings & Reviews

Keywords: wine,reviews

Rows: 150781

Sales: 0

Questions: 0

Chocolate Reviews
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Delicious data posted by Rachael Tatman on Kaggle: https://www.kaggle.com/rtatman Context Chocolate is one of the most popular candies in the world. Each year, residents of the United States collectively eat more than 2.8 billions pounds. However, not all chocolate bars are created equal! This dataset contains expert ratings of over 1,700 individual chocolate bars, along with information on their regional origin, percentage of cocoa, the variety of chocolate bean used and where the beans were grown. Flavors of Cacao Rating System: 5= Elite (Transcending beyond the ordinary limits) 4= Premium (Superior flavor development, character and style) 3= Satisfactory(3.0) to praiseworthy(3.75) (well made with special qualities) 2= Disappointing (Passable but contains at least one significant flaw) 1= Unpleasant (mostly unpalatable) Acknowledgements: These ratings were compiled by Brady Brelinski, Founding Member of the Manhattan Chocolate Society. For up-to-date information, as well as additional content (including interviews with craft chocolate makers), please see his website: Flavors of Cacao

Category: Ratings & Reviews

Keywords: chocolate,reviews,candy,confections

Rows: 1646

Sales: 0

Questions: 0