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yemeksepeti Customer Sentiment Data
Product Reviews & Feedback
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About
This collection of data consists of customer feedback on Turkish restaurants, extracted from a major food ordering website. Its primary value lies in the granular scoring system provided alongside the textual reviews, allowing researchers to perform targeted sentiment analysis concerning specific operational and culinary aspects. The data is static, with no future updates planned.
Columns
- speed: Represents the rating given for the time taken for order delivery. This column is fully populated, containing approximately 60.2 thousand valid entries.
- service: Represents the rating concerning the quality of the restaurant's service. A significant portion of this column is missing data (85%), with about 9.2 thousand valid entries.
- flavour: Represents the rating given for the taste of the food ordered. This column is sparsely populated, with roughly 95% of entries missing and only about 2.8 thousand valid entries available.
- review: Contains the actual customer comments, which are textual inputs. This column is highly sparse, with missing entries comprising around 98% of the records.
Distribution
The data is provided in a CSV file format, named
yorumsepeti.csv, which has a size of 5.49 MB. It contains four columns of information. Specific row counts or record numbers are not detailed, but the column data suggests the dataset holds over 60,000 records in total, although many fields, particularly the service, flavour, and review columns, contain a high percentage of missing values. The expected update frequency for this dataset is listed as 'Never'.Usage
This dataset is ideal for:
- Developing and testing models for aspect-based sentiment analysis, particularly those focused on multilingual or low-resource languages like Turkish.
- Text classification and opinion mining research related to the food service industry.
- Exploratory data analysis investigating consumer preferences regarding delivery speed versus flavour quality.
- Natural Language Processing (NLP) applications requiring annotated text data with specific categorical ratings.
Coverage
The data is geographically limited to restaurants reviewed on the Turkish food ordering site, thus focusing on Turkish language and culture. The time range of the data is not specified within the available information; however, the records are historical restaurant review snippets. The demographic scope includes customers who used the food ordering service and provided feedback.
License
CC BY-SA 4.0
Who Can Use It
- NLP Researchers: To train language models for Turkish sentiment analysis and text classification.
- Data Scientists: For feature engineering and predictive modelling based on customer feedback scores.
- Academic Institutions: For studies in social computing, opinion dynamics, and food service quality metrics.
- Machine Learning Developers: To create recommendation engines or tools that analyse specific aspects of service quality.
Dataset Name Suggestions
- Yorumsepeti Turkish Reviews
- Aspect-Scored Food Opinions (Turkish)
- yemeksepeti Customer Sentiment Data
- Turkish Restaurant Review Ratings
Attributes
Original Data Source: yemeksepeti Customer Sentiment Data
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