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It is a Natural Language Processing Problem where we have to decide the sentiments of the users who reviewed the course. and then classifying the reviews into positive and negative.
This dataset was used for my undergraduate research. The main problem in this dataset is its imbalanced nature.
I scraped the website of Coursera and pre-labelled the dataset depending on their rating. For a 5-star rating, the review was labelled as Very Positive, Positive for 4-star, Neutral for 3-star, Negative for 2-star, and Very Negative for 1-star. There are 2 files, reviews.tsv and reviews_by_course.tsv. The reviews.tsv file has no grouping, just the course reviews and their corresponding label. For the reviews_by_course.tsv, they are grouped by the CourseId column.
reviews.tsv Id - The unique identifier for a review.
Review - The actual course review.
Label - The rating of the course review.
reviews_by_course.tsv CourseId - The course tag. This is in the URL of the course in the Coursera website. For example, in this URL, machine-learning would be the course tag.
Review - A review in a specific course.
Label - The rating of the course review.
Coursera is an online learning platform founded by Stanford professors Andrew Ng and Daphne Koller that offers courses, specializations, and degrees. Wikipedia Founded: 2012 CEO: Jeff Maggioncalda Headquarters: Mountain View, California, United States Employees: 280 (June 2018) Number of employees: 280 (June 2018) Founders: Andrew Ng, Daphne Koller