Amazon Musical Instrument Reviews General Readme on Projects Web commerce sites get a substantial amount of feedback from reviews users post on various websites. It is not practical to go through all this information by hand to determine whether a user liked a particular product or not. For our project we are going to use a dataset of Amazon Musical Instrument Reviews. The main reason I selected this dataset is that it is significantly smaller than the Amazon review datasets for movies, music, and books. This dataset has a bit over 221,000 reviews. The columns in the dataset are name description verified whether the reviewer bought the product from Amazon or not reviewTime time of the review reviewerID ID of the reviewer, e.g. A2SUAM1J3GNN3B asin ID of the product reviewerName name of the reviewer reviewText the text of the body of the review summary the test of the heading of the review unixReviewTime time of the review (Unix time) vote the number of times other reviewers thought the review helpful style the attributes that vary -- for a example for a shirt it might be medium blue image the associated image for the item. rating what we are trying to learn to predict a positive, neutral, or negative review. You are to develop a classifier that predicts the rating. The file with the training data is at http://34.82.108.37/amazonReviewsTrain.csv.zip Once you are done training and tuning your classify you should run it on the test data. The file with the test data is http://34.82.108.37/amazonReviewsTest.csv Your predictions for the test set should be in a text file named amazonReviewsPredictions.txt that is placed in the same github directory as your Jupyter notebook file containing your code and comments. The format of this predictions file should be neutral positive positive positive neutral neutral positive positive positive positive ... To test the format of your output file you can use the following function: def test(): i = 0 answers = ['negative', 'neutral', 'positive'] with open('amazonReviewsPredictions.txt', 'r') as outfile: lines = outfile.readlines() for line in lines: if line.strip() not in answers: return("invalid entry line %i" % (i)) i += 1 if i != 10000: return ("incorrect number of lines") else: return ("file looks ok") test()

Amazon Musical Instrument Reviews General Readme on Projects Web commerce sites get a substantial amount of feedback from reviews users post on various websites. It is not practical to go through all this information by hand to determine whether a user liked a particular product or not. For our project we are going to use a dataset of Amazon Musical Instrument Reviews. The main reason I selected this dataset is that it is significantly smaller than the Amazon review datasets for movies, music, and books. This dataset has a bit over 221,000 reviews. The columns in the dataset are name description verified whether the reviewer bought the product from Amazon or not reviewTime time of the review reviewerID ID of the reviewer, e.g. A2SUAM1J3GNN3B asin ID of the product reviewerName name of the reviewer reviewText the text of the body of the review summary the test of the heading of the review unixReviewTime time of the review (Unix time) vote the number of times other reviewers thought the review helpful style the attributes that vary -- for a example for a shirt it might be medium blue image the associated image for the item. rating what we are trying to learn to predict a positive, neutral, or negative review. You are to develop a classifier that predicts the rating. The file with the training data is at http://34.82.108.37/amazonReviewsTrain.csv.zip Once you are done training and tuning your classify you should run it on the test data. The file with the test data is http://34.82.108.37/amazonReviewsTest.csv Your predictions for the test set should be in a text file named amazonReviewsPredictions.txt that is placed in the same github directory as your Jupyter notebook file containing your code and comments. The format of this predictions file should be neutral positive positive positive neutral neutral positive positive positive positive ... To test the format of your output file you can use the following function: def test(): i = 0 answers = ['negative', 'neutral', 'positive'] with open('amazonReviewsPredictions.txt', 'r') as outfile: lines = outfile.readlines() for line in lines: if line.strip() not in answers: return("invalid entry line %i" % (i)) i += 1 if i != 10000: return ("incorrect number of lines") else: return ("file looks ok") test()

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

google colab [jupyter notebook]

Amazon Musical Instrument Reviews

General Readme on Projects

Web commerce sites get a substantial amount of feedback from reviews users post on various websites. It is not practical to go through all this information by hand to determine whether a user liked a particular product or not.

For our project we are going to use a dataset of Amazon Musical Instrument Reviews. The main reason I selected this dataset is that it is significantly smaller than the Amazon review datasets for movies, music, and books. This dataset has a bit over 221,000 reviews. The columns in the dataset are

name	description
verified	whether the reviewer bought the product from Amazon or not
reviewTime	time of the review
reviewerID	ID of the reviewer, e.g. A2SUAM1J3GNN3B
asin	ID of the product
reviewerName	name of the reviewer
reviewText	the text of the body of the review
summary	the test of the heading of the review
unixReviewTime	time of the review (Unix time)
vote	the number of times other reviewers thought the review helpful
style	the attributes that vary -- for a example for a shirt it might be medium blue
image	the associated image for the item.
rating	what we are trying to learn to predict a positive, neutral, or negative review.

You are to develop a classifier that predicts the rating.

The file with the training data is at http://34.82.108.37/amazonReviewsTrain.csv.zip

Once you are done training and tuning your classify you should run it on the test data.

The file with the test data is http://34.82.108.37/amazonReviewsTest.csv

Your predictions for the test set should be in a text file named amazonReviewsPredictions.txt that is placed in the same github directory as your Jupyter notebook file containing your code and comments.

The format of this predictions file should be

neutral positive positive positive neutral neutral positive positive positive positive ...

To test the format of your output file you can use the following function:

def test(): i = 0 answers = ['negative', 'neutral', 'positive'] with open('amazonReviewsPredictions.txt', 'r') as outfile: lines = outfile.readlines() for line in lines: if line.strip() not in answers: return("invalid entry line %i" % (i)) i += 1 if i != 10000: return ("incorrect number of lines") else: return ("file looks ok") test()

Expert Solution

Trending now

This is a popular solution!

Step by step

Solved in 4 steps

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Recommended textbooks for you

Database System Concepts

Computer Science

ISBN:

9780078022159

Author:

Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:

McGraw-Hill Education