A November 2021 survey of online users in the United States found that 81 percent of respondents had used Google as a tool to evaluate local businesses in the past 12 months. Yelp was ranked second with over half of respondents using the review platform for such purpose.

The statistic shows the findings of a survey on the level of trust in online review rankings or rating systems in Sweden in 2016. When asked, if they considered online review rankings or rating systems to be reliable or not, * percent of respondents reported that they found them totally reliable.

Historical Dataset of East Shore Online is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2013-2016),Total Classroom Teachers Trends Over Years (2014-2023),American Indian Student Percentage Comparison Over Years (2013-2014),Hispanic Student Percentage Comparison Over Years (2013-2016),White Student Percentage Comparison Over Years (2013-2016),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2013-2015),Diversity Score Comparison Over Years (2013-2016),Free Lunch Eligibility Comparison Over Years (2013-2016),Reduced-Price Lunch Eligibility Comparison Over Years (2013-2016),Graduation Rate Comparison Over Years (2013-2014)

This statistic displays the findings of a survey on the distribution of felt trust in online review rankings or rating systems in the Benelux in **********. When asked, if they considered online review rankings or rating systems to be reliable or not, roughly ** percent of the Belgian respondents reported that they found them fairly reliable.

The growth of the Internet has enabled consumer-to-consumer interactions through online platforms where users share content and influence the purchase decisions of other consumers. The objective of this research is to identify the effect of perceived usefulness of online reviews on hotel booking intentions. The approach is quantitative, using a questionnaire to collect data from consumers who use online reviews before booking a hotel. The data were analyzed using structural equation modeling. The results showed the direct influence of perceived information usefulness on purchase intention, and the antecedent constructs— needs of information, information credibility, and information quality—had a positive and significant impact on perceived usefulness of online reviews. Comparing these results with research by Erkan and Evans (2016) conducted with UK consumers that use social media to decide about their purchases, in this study information credibility was more relevant than information quality, suggesting a more skeptical behavior of Brazilian consumers. These findings have implications for practitioners that manage the digital marketing of organizations inserted in this environment, mainly regarding the impact of credibility and quality of online reviews on hotel booking intentions, being this a practical contribution of the research.

Keywords: Hospitality services. Online consumer reviews. Perceived usefulness. Purchase Intention.

British Airways, one of the world's leading airlines, has been synonymous with excellence and reliability for decades. With a rich history and a commitment to providing exceptional customer experiences, British Airways continues to be a preferred choice for travelers worldwide.

As part of a challenging and rewarding data science project at British Airways, I had the opportunity to work on web scraping review data from the renowned Skytrax website. The goal was to collect valuable insights from customer reviews and leverage data-driven approaches to enhance the airline's services and customer satisfaction.

The dataset comprises the following columns, each providing essential information extracted from the reviews:

Reviews: This column contains the text-based feedback and reviews provided by customers after their experience with British Airways.

Date: The date on which the review was posted by the customer, offering valuable temporal information.

Stars: The rating given by the traveler, typically on a scale of 1 to 5 stars, reflecting their overall satisfaction with the airline's services.

Type of Traveler: This column categorizes the type of traveler who left the review, distinguishing between different travel demographics, such as business travelers, families, or solo adventurers.

Type of Seat: Provides insights into the type of seat the traveler experienced during their flight, including economy, premium economy, business, or first class.

Country: Indicates the country of origin of the customer, allowing for regional analysis and understanding customer preferences.

Recommended: A binary indicator that reflects whether the traveler would recommend British Airways based on their experience.

Route: This column provides information about the specific route or flight taken by the passengers, offering context to their reviews and experiences.

This statistic displays the findings of a survey on the distribution of felt trust in online review rankings or rating systems in the European Union (EU-28) in **********. When asked, if they considered online review rankings or rating systems to be reliable or not, ************* of respondents reported that they found them totally reliable.

-> If you use Turkish_Product_Reviews_by_Gozukara_and_Ozel_2016 dataset please cite: https://dergipark.org.tr/en/pub/cukurovaummfd/issue/28708/310341

@research article { cukurovaummfd310341, journal = {Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi}, issn = {1019-1011}, eissn = {2564-7520}, address = {Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi Yayın Kurulu Başkanlığı 01330 ADANA}, publisher = {Cukurova University}, year = {2016}, volume = {31}, pages = {464 - 482}, doi = {10.21605/cukurovaummfd.310341}, title = {Türkçe ve İngilizce Yorumların Duygu Analizinde Doküman Vektörü Hesaplama Yöntemleri için Bir Deneysel İnceleme}, key = {cite}, author = {Gözükara, Furkan and Özel, Selma Ayşe} }

https://doi.org/10.21605/cukurovaummfd.310341

-> Turkish_Product_Reviews_by_Gozukara_and_Ozel_2016 dataset is composed as below: ->-> Top 50 E-commerce sites in Turkey are crawled and their comments are extracted. Then randomly 2000 comments selected and manually labelled by a field expert. ->-> After manual labeling the selected comments is done, 600 negative and 600 positive comments are left. ->-> This dataset contains these comments.

-> English_Movie_Reviews_by_Pang_and_Lee_2004 ->-> Pang, B., Lee, L., 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts, In Proceedings of the 42nd annual meeting on Association for Computational Linguistics (p. 271). ->-> Source: https://www.cs.cornell.edu/people/pabo/movie-review-data/ | polarity dataset v2.0 - review_polarity.tar.gz

-> English_Movie_Reviews_Sentences_by_Pang_and_Lee_2005 ->-> Pang, B., Lee, L., 2005. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales, In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (pp. 115-124), Association for Computational Linguistics ->-> Source: https://www.cs.cornell.edu/people/pabo/movie-review-data/ | sentence polarity dataset v1.0 - rt-polaritydata.tar.gz

-> English_Product_Reviews_by_Blitzer_et_al_2007 ->-> Article of the dataset: Blitzer, J., Dredze, M., Pereira, F., 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification, In ACL (Vol. 7, pp. 440-447). ->-> Source: http://www.cs.jhu.edu/~mdredze/datasets/sentiment/ | processed_acl.tar.gz

-> Turkish_Movie_Reviews_by_Demirtas_and_Pechenizkiy_2013 ->-> Demirtas, E., Pechenizkiy, M., 2013. Cross-lingual polarity detection with machine translation, In Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining (p. 9). ACM. ->-> http://www.win.tue.nl/~mpechen/projects/smm/#Datasets Turkish_Movie_Sentiment.zip

-> The dataset files are provided as used in the article. -> Weka files are generated with Raw Frequency of terms rather than used Weighting Schemes

-> The folder Cross_Validation contains 10-fold cross-validation each fold files. -> Inside Cross_Validation folder, each turn of the cross-validation is named as test_X where X is the turn number -> Inside test_X folder * Test_Set_Negative_RAW: Contains raw negative class Test data of that cross-validation turn * Test_Set_Negative_Processed: Contains pre-processed negative class Test data of that cross-validation turn * Test_Set_Positive_RAW: Contains raw positive class Test data of that cross-validation turn * Test_Set_Positive_Processed: Contains pre-processed positive class Test data of that cross-validation turn * Train_Set_Negative_RAW: Contains raw negative class Train data of that cross-validation turn * Train_Set_Negative_Processed: Contains pre-processed negative class Train data of that cross-validation turn * Train_Set_Positive_RAW: Contains raw positive class Train data of that cross-validation turn * Train_Set_Positive_Processed: Contains pre-processed positive class Train data of that cross-validation turn * Train_Set_For_Weka: Contains processed Train set formatted for Weka * Test_Set_For_Weka: Contains processed Test set formatted for Weka

-> The folder Entire_Dataset contains files for Entire Dataset * Negative_Processed: Contains all negative comments processed data * Positive_Processed: Contains all positive comments processed data * Negative_RAW: Contains all negative comments RAW data * Positive_RAW: Contains all positive comments RAW data * Entire_Dataset_WEKA: Contains all documents processed data in WEKA format

Historical Dataset of Laker Online is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2016-2023),Total Classroom Teachers Trends Over Years (2017-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2017-2023),Asian Student Percentage Comparison Over Years (2021-2022),White Student Percentage Comparison Over Years (2016-2023),Two or More Races Student Percentage Comparison Over Years (2021-2022),Diversity Score Comparison Over Years (2021-2022),Free Lunch Eligibility Comparison Over Years (2021-2022),Reduced-Price Lunch Eligibility Comparison Over Years (2021-2022)

This issue of Child and Family Services Reviews Update contains the following sections: Enhancements to the Online Monitoring System, Preparing for Upcoming CFSRs, FAQs on the CFSR Information Portal, and Now in Spanish: Onsite Review Instrument and Instructions.

Metadata-only record linking to the original dataset. Open original dataset below.

These statistical tables are one of the results from a project undertaken by Statistics Canada on behalf of the Treasury Board Secretariat (TBS) in support of the Horizontal Innovation and Clean Technology Review. They were produced from program data provided by 22 federal government departments and Crown corporations and their subsequent integration into Statistics Canada’s Linkable File Environment (LFE), which comprises a large number of administrative and survey data linked at the enterprise level. More than 430,000 individual records were collected, from 98 program streams over the 2007-2016 period. Program streams were also grouped in seven aggregate categories: grants, repayable contributions, non-repayable contributions, conditional repayable contributions, financing, government performed services and other. Program recipients at the enterprise level (whether for-profit or public entities) were matched to Statistics Canada’s Business Register (BR), which contains all active enterprises in Canada, and then linked to the LFE using both deterministic (Business Numbers) and probabilistic techniques. A high match rate was achieved, representing 89.4% of all records and 96.6% of funds, corresponding to 88,415 unique recipient enterprises over the reference period. Relevant data for these enterprises, such as financial and employment variables, industry, location, profit and exporter status, were then extracted from the LFE.

This issue of Child and Family Services Reviews Update contains the following sections: Modifications to the Use of CFSR Statewide Data Indicators, Online Monitoring System: Modifications, Year 3 CFSR News: Six States Request Traditional Reviews, NEW on the CFSR Portal: Round 3 Local Site Coordinator Toolkit, and Overview of the CFSR Unit. Metadata-only record linking to the original dataset. Open original dataset below.

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.

This list provides the individual 2016 Statistics for the Animal Rescues and Animal Shelters that are PACFA licensed in the State of Colorado as of September 15, 2017. The numbers in this data set were provided by each individual facility, if you have questions about the numbers please call the facility directly.

If you have general questions please send an email to cda_pacfa@state.co.us. If you are aware of a facility that is not licensed please complete a complaint form on our website (www.colorado.gov/aginspection/pacfa) so that we may investigate the reason they are not currently licensed.

Facilities marked with ** have possible issues with their submission numbers. These possible issues were determined by the statistics review group.

Disclaimer: Although PACFA requires this data to be submitted and takes all care possible to ensure the validity of this data, we do not control, and therefore guarantee, the complete accuracy, completeness and availability of data. The CDA-PACFA is not responsible for any issues that may arise from the use of this data.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This data set contains:

• Full data files of a 2016 survey of attitudes to Open Peer Review in xls and csv formats
• Survey questions (pdf)
• Readme file (txt)

Between 8 September and 7 October 2016, OpenAIRE held a survey designed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauging current levels of experience and reservation with different types of OPR. A supplementary aim was to collect feedback on a provisional definition of OPR as created during another strand of work. The survey aimed to aid the development of appropriate OPR approaches by providing evidence about the attitudes of authors, editors and reviewers towards OPR, their reservations and needs, as well as to gauge current levels of experience and reservations with different types of OPR. The survey was conducted via an openly accessible online questionnaire (using the scientific survey platform SoSci, www.soscisurvey.de). It received a total of 3062 complete responses (a further 635 responses were discarded as incomplete). The survey was open to all wishing to take part and distributed via social media, scholarly communications mailing lists, publisher newsletters and, in one case, a publisher internal mailing list (Copernicus Publications).

Acknowledgement: This work is funded by the European Commission H2020 project OpenAIRE2020 (Grant agreement: 643410, Call: H2020-EINFRA-2014-1)

Contact: Dr Tony Ross-Hellauer, University of Göttingen, State and University Library, ross-hellauer@sub.uni-goettingen.de

Context

This data set is related to the review and rating of the movies across different Genres.

Content

ID: Id provided for each movie review Movie: Name of the movie Year: The release year of the movie Genres: Genres of the movie i.e. Action, sci-fi, horror, etc Review: Raw review consisting of text and emoji Rating: Rating varies from 1 to 5

Acknowledgements

I want to thank all the reviewers who have given their valuable reviews in the Google review section, without your effort it cannot be possible for me to enlarge the dataset.

Inspiration

Kaggler

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for public elementary and secondary schools included in the NCES Common Core of Data (CCD). The CCD is an annual collection of basic administrative characteristics that includes the physical address for all public schools, school districts, and state education agencies in the United States. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for schools and school district administrative offices based on these addresses. The point locations in this data layer were developed from the 2016-2017 CCD collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations.All information contained in this file is in the public _domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Global anthropogenic CO2 emissions based on EDGARv4.3, fuel type and category specific emissions provided by Greet Janssens-Maenhout (EU-JRC), BP statistics 2016 (http://www.bp.com/content/dam/bp/excel/energy-economics/statistical-review-2016/bp-statistical-review-of-world-energy-2016-workbook.xlsx), temporal variations based on MACC-TNO (https://gmes-atmosphere.eu/documents/deliverables/d-emis/MACC_TNO_del_1_3_v2.pdf), temporal extrapolation and disaggregation described in COFFEE (Steinbach et al. 2011). Gerbig, C., Janssens-Maenhout, G., Karstens, U. (2017). Global anthropogenic CO2 emissions based on EDGARv4.3 and BP statistics 2016, 2009-08-01–2009-08-31, https://hdl.handle.net/11676/-Ds8OPhCs4jTWMyTVyH9C5Xg

Historical Dataset of Auhs Online Acadamy is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2016-2023),Distribution of Students By Grade Trends,Hispanic Student Percentage Comparison Over Years (2016-2022),White Student Percentage Comparison Over Years (2016-2023),Diversity Score Comparison Over Years (2016-2022),Reduced-Price Lunch Eligibility Comparison Over Years (2016-2023),Graduation Rate Comparison Over Years (2017-2023)