Thinking and understanding
Center on: Star Data Modeling Comments
Briefly ideas: understood as Jingdong Taobao Mall comment data, explain how to index 4.8 stars, you have little impact on the level of a comment product?
- Personal habits is big data problems Chapter IV alone write data cleansing, specific processes see the country awards paper group digital-analog graduate.
- First three annexes to analyze, extract useful variables, delete data stripe field missing categorical variables normalized, and further processing to wait for updates.
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Star rating model basis.
First screening data from 15 variables valid variables, analysis of the distribution of each variable was constructed STAR_RATING relationship between the number of votes, consumption patterns, consumer and other sources. Your model may be qualitative as well as quantitative.
The problem eventually settled in the analysis of the mode of the three products after processing a simple excel each team can work something out. Highlights on the beautiful pattern mining validity and charts. -
Knowledge mining models (natural language processing)
The second question larger workload. For each product, requiring major impact indicators analysis ① star ratings (principal component analysis, hierarchical clustering, factor analysis model), ② the reputation of each mode change over time (time series analysis) based on, ③ determined based on the text of measure (note that standard, the need for rules) to give the corresponding success or failure implied. (Frequency, such as a review of certain words appear in a pattern + = a time series of products to be cool)
. Question two can be separated to create multiple models. -
Write a 1-2 page letter, provide your text mining results to the director of marketing, to have the data support (chart).
M Award cut-off point: the model for analysis, make recommendations to improve the reputation of a product or sales
The problem is relatively difficult, although attractive big data problems, but because the more critical variable text, natural language processing for the students did not experience may be difficult to make the results of careful consideration.
Translation of the original title
Data interpretation
original
2020 MCM Weekend 2
Problem C: A Wealth of Data
In the online marketplace it created, Amazon provides customers with an opportunity to rate and
review purchases. Individual ratings - called “star ratings” – allow purchasers to express their
level of satisfaction with a product using a scale of 1 (low rated, low satisfaction) to 5 (highly
rated, high satisfaction). Additionally, customers can submit text-based messages – called
“reviews” – that express further opinions and information about the product. Other customers
can submit ratings on these reviews as being helpful or not – called a “helpfulness rating” –
towards assisting their own product purchasing decision. Companies use these data to gain
insights into the markets in which they participate, the timing of that participation, and the
potential success of product design feature choices.
Sunshine Company is planning to introduce and sell three new products in the online
marketplace: a microwave oven, a baby pacifier, and a hair dryer. They have hired your team as
consultants to identify key patterns, relationships, measures, and parameters in past customersupplied ratings and reviews associated with other competing products to 1) inform their online
sales strategy and 2) identify potentially important design features that would enhance product
desirability. Sunshine Company has used data to inform sales strategies in the past, but they have
not previously used this particular combination and type of data. Of particular interest to
Sunshine Company are time-based patterns in these data, and whether they interact in ways that
will help the company craft successful products.
To assist you, Sunshine’s data center has provided you with three data files for this project:
hair_dryer.tsv, microwave.tsv, and pacifier.tsv. These data represent customer-supplied
ratings and reviews for microwave ovens, baby pacifiers, and hair dryers sold in the Amazon
marketplace over the time period(s) indicated in the data. A glossary of data label definitions is
provided as well. THE DATA FILES PROVIDED CONTAIN THE ONLY DATA YOU
SHOULD USE FOR THIS PROBLEM.
Requirements
- Analyze the three product data sets provided to identify, describe, and support with
mathematical evidence, meaningful quantitative and/or qualitative patterns, relationships,
measures, and parameters within and between star ratings, reviews, and helpfulness ratings that
will help Sunshine Company succeed in their three new online marketplace product offerings. - Use your analysis to address the following specific questions and requests from the Sunshine
Company Marketing Director:
a. Identify data measures based on ratings and reviews that are most informative for
Sunshine Company to track, once their three products are placed on sale in the online
marketplace.
b. Identify and discuss time-based measures and patterns within each data set that might
suggest that a product’s reputation is increasing or decreasing in the online marketplace.
c. Determine combinations of text-based measure(s) and ratings-based measures that best
indicate a potentially successful or failing product.d. Do specific star ratings incite more reviews? For example, are customers more likely to
write some type of review after seeing a series of low star ratings?
e. Are specific quality descriptors of text-based reviews such as ‘enthusiastic’,
‘disappointed’, and others, strongly associated with rating levels? - Write a one- to two-page letter to the Marketing Director of Sunshine Company summarizing
your team’s analysis and results. Include specific justification(s) for the result that your team
most confidently recommends to the Marketing Director.
Your submission should consist of:
One-page Summary Sheet
Table of Contents
One- to Two-page Letter
Your solution of no more than 20 pages, for a maximum of 24 pages with your summary
sheet, table of contents, and two-page letter.
Note: Reference List and any appendices do not count toward the page limit and should appear
after your completed solution. You should not make use of unauthorized images and materials
whose use is restricted by copyright laws. Ensure you cite the sources for your ideas and the
materials used in your report.
Glossary
Helpfulness Rating: an indication of how valuable a particular product review is when
making a decision whether or not to purchase that product.
Pacifier: a rubber or plastic soothing device, often nipple shaped, given to a baby to suck
or bite on.
Review: a written evaluation of a product.
Star Rating: a score given in a system that allows people to rate a product with a number
of stars.
Attachments: The Problem Datasets
Problem_C_Data.zip
The three data sets provided contain product user ratings and reviews extracted from the
Amazon Customer Reviews Dataset thru Amazon Simple Storage Service (Amazon S3).
hair_dryer.tsv
microwave.tsv
pacifier.tsvData Set Definitions: Each row represents data partitioned into the following columns.
● marketplace (string): 2 letter country code of the marketplace where the review was
written.
● customer_id (string): Random identifier that can be used to aggregate reviews written by
a single author.
● review_id (string): The unique ID of the review.
● product_id (string): The unique Product ID the review pertains to.
● product_parent (string): Random identifier that can be used to aggregate reviews for the
same product.
● product_title (string): Title of the product.
● product_category (string): The major consumer category for the product.
● star_rating (int): The 1-5 star rating of the review.
● helpful_votes (int): Number of helpful votes.
● total_votes (int): Number of total votes the review received.
● vine (string): Customers are invited to become Amazon Vine Voices based on the trust
that they have earned in the Amazon community for writing accurate and insightful
reviews. Amazon provides Amazon Vine members with free copies of products that have
been submitted to the program by vendors. Amazon doesn’t influence the opinions of
Amazon Vine members, nor do they modify or edit reviews.
● verified_purchase (string): A “Y” indicates Amazon verified that the person writing the
review purchased the product at Amazon and didn’t receive the product at a deep
discount.
● review_headline (string): The title of the review.
● review_body (string): The review text.
● review_date (bigint): The date the review was written.
