Dataset_Datasheet Questions

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Instructions: Please fill out the following form, 1-2 sentences per question unless more is needed. If you cannot find a particular answer for a question, just mark Missing Information in the response if the dataset does not provide details on it. For any questions where you feel the dataset has a potential risk/harm, put the word Alert in the answer (you will use this later in the next part of the assignment). Title of Dataset: Airlines Reviews and Rating Authors/Creators: ANANDSHAW2001 Link to webpage: https://www.kaggle.com/anandshaw2001 Short description: A comprehensive collection of passenger feedback on various aspects of their flight experiences across different airlines. Motivation: 1. For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description. This dataset aims to provide insights into passenger satisfaction and airlines' service quality. 2. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? ANANDSHAW2001, a reviewer created the dataset. 3. Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. It was not funded by anyone. 4. Any other comments? N/A Composition: 1. What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description. There was a chart built including different types of airlines. There were charts and documents. 2. How many instances are there in total (of each type, if appropriate)? 2 in total. 3. Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).
No it does not. 4. What data does each instance consist of? “Raw” data (e.g., unprocessed text or images) or features? In either case, please provide a description. Each data contains unprocessed texts and numbers which were built into a chart form. 5. Is there a label or target associated with each instance? If so, please provide a description. There are no label or target associated to each instance. 6. Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. Most of the general information are included but missing specific category like foods, passengers, etc. 7. Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit. No they were not made explicit. 8. Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them. No there are no recommended data splits. 9. Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. There were no redundancies or any errors on this dataset. 10. Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate. There were no external resources linked to this dataset. 11. Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor– patient confidentiality, data that includes the content of individuals’ non-public communications)? If so, please provide a description. All of the data in this dataset were reviews from different reviewers so it is not considered as confidential.
12. Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. No it does not. If the dataset does not relate to people, you may skip the remaining questions in this section. 13. Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset. 14. Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset? If so, please describe how. 15. Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals race or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)? If so, please provide a description. 16. Any other comments? Collection Process: 1. How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If the data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how. It was all survey response and straight from different customers on different airlines. If the data was reported, it should be validated or verified due to the agreement of providing reviews from these customers. 2. What mechanisms or procedures were used to collect the data (e.g., hardware apparatuses or sensors, manual human curation, software programs, software APIs)? How were these mechanisms or procedures validated? It was through google which was used as a survey. 3. If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? Probabilistic with specific sampling probabilities. 4. Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? It was not necessary for any specific type of people, anyone who had experience of certain type of airlines.
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