Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Access our Trustpilot Reviews Data in CSV Format, offering a comprehensive collection of customer reviews from Trustpilot.

This dataset includes detailed reviews, ratings, and feedback across various industries and businesses. Available in a convenient CSV format, it is ideal for market research, sentiment analysis, and competitive benchmarking.

Leverage this data to gain insights into customer satisfaction, identify trends, and enhance your business strategies. Whether you're analyzing consumer sentiment or conducting competitive analysis, this dataset provides valuable information to support your needs.

Yearly data of Quality Review ratings from 2005 to 2017

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imdb_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

The Agency performs ongoing assessment of New York City streets. Ratings are based on a scale from 1 to 10, and results are grouped in the following categories: - Good (%) - ratings of 8 to 10 - Fair (%) - ratings of 4 to 7 (except on local streets where a 7 is good) - Poor (%) - ratings of 1 to 3. Inspectors rate street conditions throughout the five boroughs using the scale above.

You can analyze the Yelp's data the OpenWeb Ninja API provides to gain insights into the business world. This includes looking at market trends, identifying popular business categories, reading customer reviews and ratings, and understanding the factors that contribute to business success or failure.

The dataset includes all key business listings data & consumer review data:

Business Type, Description, Categories, Location, Consumer Review Data, Review Rating, Review Reactions, Review Author Information, Licenses, Highlights, and more!

Access a wealth of entertainment data with our OTT (Over-the-Top) scraping service. Gather music, movie, and IMDB reviews & ratings data effortlessly. Extract Data from popular platforms like Netflix, Hulu, Spotify, IMDb, and more

Utilize our Amazon reviews dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding customer behavior, product performance, and market trends, empowering organizations to refine their product and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements. Popular use cases include: Product Performance Analysis: Analyze Amazon reviews to assess product performance, uncovering customer satisfaction levels, common issues, and highly praised features to inform product improvements and marketing messages. Customer Behavior Insights: Gain insights into customer behavior, purchasing patterns, and preferences, enabling more personalized marketing and product recommendations. Demand Forecasting: Leverage Amazon reviews to predict future product demand by analyzing historical review data and identifying trends, helping to optimize inventory management and sales strategies. Accessing and analyzing the Amazon reviews dataset supports market strategy optimization by leveraging insights to analyze key market trends and customer preferences, enhancing overall business decision-making.

The MBTA GTFS Pre-rating Recap collection contains text files that describe a generalized MBTA schedule for a specific season. While the MBTA posts all previously published GTFS files, this collection makes it easier to find the schedule that the MBTA ran for the majority of a season instead of having to identify the "correct" one from the GTFS archive.We recommend using these files instead of the current GTFS when doing historical analyses.Data dictionary:https://github.com/mbta/gtfs-documentation/blob/master/reference/gtfs.mdTo view all previously published GTFS files, please refer to the link below:https://github.com/mbta/gtfs-documentation/blob/master/reference/gtfs-archive.mdMassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

Film Classification and Related Information Inquiry from 2015 to 2022

Amazon Customer Reviews (a.k.a. Product Reviews) is one of Amazons iconic products. In a period of over two decades since the first review in 1995, millions of Amazon customers have contributed over a hundred million reviews to express opinions and describe their experiences regarding products on the Amazon.com website. This makes Amazon Customer Reviews a rich source of information for academic researchers in the fields of Natural Language Processing (NLP), Information Retrieval (IR), and Machine Learning (ML), amongst others. Accordingly, we are releasing this data to further research in multiple disciplines related to understanding customer product experiences. Specifically, this dataset was constructed to represent a sample of customer evaluations and opinions, variation in the perception of a product across geographical regions, and promotional intent or bias in reviews.

Over 130+ million customer reviews are available to researchers as part of this release. The data is available in TSV files in the amazon-reviews-pds S3 bucket in AWS US East Region. Each line in the data files corresponds to an individual review (tab delimited, with no quote and escape characters).

Each Dataset contains the following columns:

• marketplace: 2 letter country code of the marketplace where the review was written.
• customer_id: Random identifier that can be used to aggregate reviews written by a single author.
• review_id: The unique ID of the review.
• product_id: The unique Product ID the review pertains to. In the multilingual dataset the reviews for the same product in different countries can be grouped by the same product_id.
• product_parent: Random identifier that can be used to aggregate reviews for the same product.
• product_title: Title of the product.
• product_category: Broad product category that can be used to group reviews (also used to group the dataset into coherent parts).
• star_rating: The 1-5 star rating of the review.
• helpful_votes: Number of helpful votes.
• total_votes: Number of total votes the review received.
• vine: Review was written as part of the Vine program.
• verified_purchase: The review is on a verified purchase.
• review_headline: The title of the review.
• review_body: The review text.
• review_date: The date the review was written.

Abstract: With the e-commerce growth, more people are buying products over the internet. To increase customer satisfaction, merchants provide spaces for product and service reviews. Products with positive reviews attract customers, while products with negative reviews lose customers. Following this idea, some individuals and corporations write fake reviews to promote their products and services or defame their competitors. The difficulty for finding these reviews was in the large amount of information available. One solution is to use data mining techniques and tools, such as the classification function. Exploring this situation, the present work evaluates classification techniques to identify fake reviews about products and services on the Internet. The research also presents a literature systematic review on fake reviews. The research used 8 classification algorithms. The algorithms were trained and tested with a hotels database. The CONCENSO algorithm presented the best result, with 88% in the precision indicator. After the first test, the algorithms classified reviews on another hotels database. To compare the results of this new classification, the Review Skeptic algorithm was used. The SVM and GLMNET algorithms presented the highest convergence with the Review Skeptic algorithm, classifying 83% of reviews with the same result. The research contributes by demonstrating the algorithms ability to understand consumers’ real reviews to products and services on the Internet. Another contribution is to be the pioneer in the investigation of fake reviews in Brazil and in production engineering.

This dataset contains ratings for inspected property features. Each row represents a rating for a single feature. Data Dictionary and User Guide can be found here. A complete list of all datasets in the series can be found here.

Gain valuable customer feedback for specific locations with daily updated Google Reviews & Ratings data. Perfect for businesses looking to analyze sentiment, track performance, and benchmark against competitors.

Description: This dataset provides comprehensive movie statistics compiled from multiple sources, including Wikipedia, The Numbers, and IMDb. It offers a rich collection of information and insights into various aspects of movies, such as movie titles, production dates, genres, runtime minutes, director information, average ratings, number of votes, approval index, production budgets, domestic gross earnings, and worldwide gross earnings.

The dataset combines data scraped from Wikipedia, which includes details about movie titles, production dates, genres, runtime minutes, and director information, with data from The Numbers, a reliable source for box office statistics. Additionally, IMDb data is integrated to provide information on average ratings, number of votes, and other movie-related attributes.

With this dataset, users can analyze and explore trends in the film industry, assess the financial success of movies, identify popular genres, and investigate the relationship between average ratings and box office performance. Researchers, movie enthusiasts, and data analysts can leverage this dataset for various purposes, including data visualization, predictive modeling, and deeper understanding of the movie landscape.

Features: - Movie_title - Production_date - Genres - Runtime_minutes - Director_name (primaryName) - Director_professions (primaryProfession) - Director_birthYear - Director_deathYear - Movie_averageRating : refers to the average rating given by online users for a particular movie - Movie_numberOfVotes : refers to the number of votes given by online users for a particular movie - Approval_Index :is a normalized indicator (on scale 0-10) calculated by multiplying the logarithm of the number of votes by the average users rating. It provides a concise measure of a movie's overall popularity and approval among online viewers, penalizing both films that got too few reviews and blockbusters that got too many. - Production_budget ( $) - Domestic_gross ($) - Worldwide_gross ($)

Potential Applications:

Box office analysis: Analyze the relationship between production budgets, domestic and worldwide gross earnings, and profitability. Genre analysis: Identify the most popular genres based on movie counts and analyze their performance. Rating analysis: Explore the relationship between average ratings, number of votes, and financial success. Director analysis: Investigate the impact of directors on movie ratings and financial performance. Time-based analysis: Study movie trends over different production years and observe changes in production budgets, box office earnings, and genre preferences. By utilizing this dataset, users can gain valuable insights into the movie industry and uncover patterns that can inform decision-making, market research, and creative strategies.

Gain credit ratings, risk analysis, and research for stocks, bonds, and government entities with Fitch Ratings, covering over 3,000 corporate entities globally.

An important indicator of the financial strength of governmental entity is its bond rating. The bond rating is similar in nature to the credit score of an individual – the higher the score, the better the ability to borrow money to finance purchases at a lower interest rate. Similarly, the higher the bond rating for a governmental entity, the more opportunities to borrow money for capital needs at lower interest rates. A high bond rating is in excellent indicator of the overall financial health of a government.This measure is obtained each year when the city seeks to issue bonds to finance its’ projects. As part of this process, bond ratings are always obtained from the rating agencies: Standard & Poor’s. Fitch Ratings and Moody's Investor Service.This page provides data for the Bond Rating performance measure.Bond ratings are a reflection of the financial strength of an entity. A high rating means an entity can issue bonds to finance capital projects at lower interest rates; lower rates result in less interest to be paid on the repayment of the bonds. Ultimately, this lowers the costs of our capital projects to our taxpayers.The performance measure dashboard is available at 5.04 Bond Rating.Additional InformationSource: Standard & Poors, Moody's Investor Service, and Fitch Ratings are the major bond rating agencies in the United States and are widely used by governmental and non-governmental entities throughout the country.Contact: Jerry HartContact E-Mail: Jerry_Hart@tempe.govData Source Type: ExcelPreparation Method: ManualPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

1) Data introduction • Apple-iphone-se-reviews dataset is a dataset that scrapes data from the Flipkart website using Selenium and BeautifulSoup links.

2) Data utilization (1)Apple-iphone-se-reviews data has characteristics that: • User ratings for Apple iPhone SE on Indian e-commerce website Flipkart are . We aim at NLP text classification through user ratings, review titles, and review text. (2)Apple-iphone-se-reviews data can be used to: • Rating prediction: You can support automated review analysis and summarization by developing machine learning models to predict ratings based on review text. • Product Improvement: Insights gained from reviews can help us identify common issues and areas for improvement in iPhone SE and guide product development and quality improvements.

This dataset provides estimated hourly dynamic line ratings for ~84,000 transmission lines across the contiguous United States from 2007-2013. The calculation methods are described in the presentation linked below, and the associated open-source Python code repository is linked in the Resources section below.

Abbreviations used in filenames and descriptions are: - SLR: static line ratings - ALR: ambient-temperature-adjusted line ratings - NLR: ambient-temperature- and day/night-irradiance-adjusted line ratings - CLR: ambient-temperature- and clear-sky-irradiance-adjusted line ratings - ILR: ambient-temperature- and measured-irradiance-adjusted line ratings - DLR: full dynamic line ratings (including air temperature/pressure, wind speed/direction, and measured irradiance)

Transmission lines are referenced by their ID in the Homeland Infrastructure Foundation-Level Data (HIFLD) on Transmission Lines (linked in Resources section). Time indices are in UTC. The data files contain ratios between modeled hourly ratings and modeled static ratings. Columns are indexed by HIFLD ID; rows are indexed by hourly timestamps from 2007-2013 (UTC). A data directory is also included in the Resources section.

The SLR files contain modeled static ratings (the denominator of the ratios in the files described above) in amps. As described in the presentation linked in the Resources section below, SLR calculations assume an ambient air temperature of 40 C, air pressure of 101 kPa, wind speed of 2 feet per second (0.61 m/s) perpendicular to the conductor, global horizontal irradiance of 1000 W/m^2, and conductor absorptivity and emissivity of 0.8. Conductor assumptions are Linnet for ~69 kV and below, Condor for ~115 kV, Martin for ~230 kV, and Cardinal for ~345 kV and above.

Caveats and Limitations

Results are sensitive to the weather data used. Validation studies on the WIND Toolkit and NSRDB are available at: - King, J. et al. "Validation of Power Output for the WIND Toolkit", 2014 (https://www.nrel.gov/docs/fy14osti/61714.pdf) - Draxl, C. et al. "Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit", 2015 (https://www.nrel.gov/docs/fy15osti/61740.pdf) - Sengupta, M. et al. "Validation of the National Solar Radiation Database (NSRDB) (2005-2012)", 2015 (https://www.nrel.gov/docs/fy15osti/64981.pdf) - Habte, A. et al. "Evaluation of the National Solar Radiation Database (NSRDB Version 2): 1998-2015", 2017 (https://www.nrel.gov/docs/fy17osti/67722.pdf)

More work is required to determine how well ratings calculated from NSRDB and WIND Toolkit data reflect the actual ratings observed by installed sensors (such as sag or tension monitors). In general, ratings calculated from modeled weather data are not a substitute for direct sensor data.

Assuming a single representative conductor type (ACSR of a single diameter) for each voltage level is an important simplification; reported line ratings at a given voltage level can vary widely.

HIFLD line routes are primarily based on imagery instead of exact construction data and may have errors.

We use historical weather data directly; calculated line ratings are thus more indicative of real-time ratings than forecasted ratings

Abstract Purpose: The objective of this study is to analyze the impact of changes in credit ratings on the long-term return of Brazilian firms. Design/methodology/approach: We conducted an event study to measure how stock prices in the Brazilian stock exchange (B3) react to rating upgrades and downgrades by Moody’s and S&P. Findings: Our sample presents positive and significant returns measured by the BHAR for ratings downgrades and non-significant ones for upgrades. Our data also show the important role of the previous rating in explaining these results in a non-linear fashion. Originality/value: Our research makes an important contribution to the theory of market efficiency, analyzing the degree of information present in the announcements of credit ratings changes. We also present results for Brazilian companies, correcting gaps pointed out in previous methodologies.