A complete list of live websites using the Star Rating technology, compiled through global website indexing conducted by WebTechSurvey.

Based on a 2022 analysis, the product display page (PDP) views experience the highest surge beyond the ***-star rating threshold. While products with an average rating from *** to **** generate the most traffic and receive the highest number of reviews, consumers remain hesitant when confronted with an average rating of *** stars.

Websites that display reviews from other users encourage shoppers to complete their purchases. According to a 2022 global study, sites selling home appliances and electronics that display ratings and reviews increased conversion rates by ** percent. Likewise, online clothing stores saw conversion rates increase by ** percent. However, the musical instruments niche saw the most striking change. Through impressions from online reviews, conversion rates rose by more than ** percent.

A complete list of live websites using the Rating System technology, compiled through global website indexing conducted by WebTechSurvey.

Rating of academies obtained using the PageRank algorithm

Product evaluation portals on the web that collect product ratings provide an excellent opportunity to observe opinion sharing in a natural setting. Evidence across different paradigms shows that minority opinions are shared less than majority opinions. This article reports a study testing whether this effect holds on product evaluation portals. We tracked the ratings of N = 76 products at 12 measurement points. We predicted that the higher (lower) the mean initial rating of a product, the more positive (negative) the newly contributed ratings will differ from this baseline – as an indication of the preferred sharing of majority compared to minority opinions. We found, however, that newly added ratings were on average less extreme than earlier ratings. These results can either be interpreted as regression to the mean or evidence for the preferred sharing of minority opinions.

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.

Look up your address and click on the map for fire protection and insurance (ISO) information. Contact the NC Department of Insurance http://www.ncdoi.com/OSFM/Ratings_and_Inspections/Default.aspx?field1=Contacts to appeal an ISO rating.

OpenWeb Ninja's Product Data API provides Product Data, Product Reviews Data, Product Offers, sourced in real-time from Google Shopping - the largest product listings aggregate on the web, listing products from all publicly available e-commerce sites (Amazon, eBay, Walmart + many others).

The API covers more than 35 billion Product Data Listings, including Product Reviews and Product Offers across the web. The API provides over 40 product data points including prices, rating and reviews insights, product details and specs, typical price ranges, and more.

OpenWeb Ninja's Product Data common use cases: - Price Optimization & Price Comparison - Market Research & Competitive Analysis - Product Research & Trend Analysis - Customer Reviews Analysis

OpenWeb Ninja's Product Data Stats & Capabilities: - 35B+ Product Listings - 40+ data points per job listing - Global aggregate - Search by keyword or GTIN/EAN

The data provide information about awareness of and interaction with physician rating websites in Austria.

In 2021, Google's share of online reviews increased to 71 percent, up from 67 percent in 2020, indicating a rise in willingness from consumers to share their experiences and opinions online. Overall, Google is the platform and search engine on which most consumers leave reviews for local businesses.

The statistic shows the quality rating of Rai apps in Italy as of 2018. As of the survey period, the Rai Play app scored the highest grade with ***, followed by Rai Play Radio which reported an average score of ***.

This dataset tracks annual overall school rank from 2010 to 2015 for W.e.b. Dubois High School

The global Consumer Ratings and Reviews Platform market is experiencing robust growth, driven by the increasing reliance of consumers on online reviews before making purchasing decisions and businesses' need to understand and manage their online reputation. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key trends, including the rise of e-commerce, the increasing adoption of social media, and the growing demand for transparency and authenticity from brands. Businesses across various sectors, including retail, logistics, and healthcare, are actively investing in these platforms to enhance customer engagement, improve brand perception, and drive sales. The cloud-based segment holds a significant market share due to its scalability, flexibility, and cost-effectiveness. Geographic expansion is also a prominent factor, with North America currently dominating the market, followed by Europe and Asia-Pacific. However, emerging markets in Asia-Pacific and the Middle East & Africa present lucrative opportunities for future growth. Competitive intensity is high, with numerous established players and new entrants vying for market share. The market's future trajectory will be shaped by factors such as the evolving landscape of online reviews, the integration of AI-powered sentiment analysis, and the growing emphasis on data privacy and security. While the market is flourishing, challenges remain. The increasing sophistication of fake reviews presents a significant threat to the credibility of these platforms, necessitating robust verification mechanisms. Furthermore, regulatory scrutiny around data privacy and consumer protection is intensifying, requiring platform providers to comply with evolving legal frameworks. Despite these challenges, the long-term outlook for the Consumer Ratings and Reviews Platform market remains positive, driven by the enduring importance of consumer feedback and the continuous innovation within the sector. The diverse applications across multiple industry verticals will fuel this growth, with increasing adoption in emerging markets contributing to this expansion in the coming years.

Rating of mathematicians obtained using the "least squares" method.

This dataset tracks annual distribution of students across grade levels in W.e.b. Dubois Academy

Content

This is a huge dataset that contains every web series around the globe streaming right now at the date of the creation of the dataset.

Inspiration

This dataset can be used to answer the following questions: - Which streaming platform(s) can I find this web series on? - Average IMDb rating and other ratings - What is the genre of the title? - What is the synopsis? - How many seasons are there right now? - Which year this was produced?

Rating of mathematicians, obtained by selecting coefficients based on expert estimates.

Advancing Homepage2Vec with LLM-Generated Datasets for Multilingual Website Classification

This dataset contains two subsets of labeled website data, specifically created to enhance the performance of Homepage2Vec, a multi-label model for website classification. The datasets were generated using Large Language Models (LLMs) to provide more accurate and diverse topic annotations for websites, addressing a limitation of existing Homepage2Vec training data.

Key Features:

LLM-generated annotations: Both datasets feature website topic labels generated using LLMs, a novel approach to creating high-quality training data for website classification models.

Improved multi-label classification: Fine-tuning Homepage2Vec with these datasets has been shown to improve its macro F1 score from 38% to 43% evaluated on a human-labeled dataset, demonstrating their effectiveness in capturing a broader range of website topics.

Multilingual applicability: The datasets facilitate classification of websites in multiple languages, reflecting the inherent multilingual nature of Homepage2Vec.

Dataset Composition:

curlie-gpt3.5-10k: 10,000 websites labeled using GPT-3.5, context 2 and 1-shot

curlie-gpt4-10k: 10,000 websites labeled using GPT-4, context 2 and zero-shot

Intended Use:

Fine-tuning and advancing Homepage2Vec or similar website classification models

Research on LLM-generated datasets for text classification tasks

Exploration of multilingual website classification

Additional Information:

Project and report repository: https://github.com/CS-433/ml-project-2-mlp

Acknowledgments:

This dataset was created as part of a project at EPFL's Data Science Lab (DLab) in collaboration with Prof. Robert West and Tiziano Piccardi.

IntroductionThroughout the COVID-19 pandemic, many patients have sought medical advice on online medical platforms. Review data have become an essential reference point for supporting users in selecting doctors. As the research object, this study considered Haodf.com, a well-known e-consultation website in China.MethodsThis study examines the topics and sentimental change rules of user review texts from a temporal perspective. We also compared the topics and sentimental change characteristics of user review texts before and after the COVID-19 pandemic. First, 323,519 review data points about 2,122 doctors on Haodf.com were crawled using Python from 2017 to 2022. Subsequently, we employed the latent Dirichlet allocation method to cluster topics and the ROST content mining software to analyze user sentiments. Second, according to the results of the perplexity calculation, we divided text data into five topics: diagnosis and treatment attitude, medical skills and ethics, treatment effect, treatment scheme, and treatment process. Finally, we identified the most important topics and their trends over time.ResultsUsers primarily focused on diagnosis and treatment attitude, with medical skills and ethics being the second-most important topic among users. As time progressed, the attention paid by users to diagnosis and treatment attitude increased—especially during the COVID-19 outbreak in 2020, when attention to diagnosis and treatment attitude increased significantly. User attention to the topic of medical skills and ethics began to decline during the COVID-19 outbreak, while attention to treatment effect and scheme generally showed a downward trend from 2017 to 2022. User attention to the treatment process exhibited a declining tendency before the COVID-19 outbreak, but increased after. Regarding sentiment analysis, most users exhibited a high degree of satisfaction for online medical services. However, positive user sentiments showed a downward trend over time, especially after the COVID-19 outbreak.DiscussionThis study has reference value for assisting user choice regarding medical treatment, decision-making by doctors, and online medical platform design.