hilaler/natural-language-processing-on-kindle-text-review

In this first experiment, we were asked to explore and experiment with language modeling with N-grams and neural-based ones. The corpus we use for both methods is all_kindle_review.csv, an English text corpus containing book reviews including the rate or value of each book by the reviewer. There is a data that contains reviews and readers' feelings towards a kindle book. They also give a rating to the book. Then the data will be classified based on the rating they provide, and find predictions with the new Metadata review: salty = ID of product helpful = indicates how helpful the rating given example: 8/10. rating = Rating of the product. reviewText = reviews from users. reviewTime = time spent reviewing. reviewerID = ID of reviewer reviewerName = name of the reviewer. summary = brief note from the reviewer. unixReviewTime = timestamp. The programming language we use is Python.

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