1. Understanding Connections: Amazon Customer Reviews
By Ryan Penn

2. Introduction
Objective
Methodology
Related Works
Questions
Overview

3. Introduction Recommender systems Online customer reviews
Greatly determines whether or not you are going to purchase the product
Reviews range in thoroughness and comprehensibility
Can play a big role in the behavior and success of your market
Market acceptance, value over time

4. Objective
Accurately predict product ratings by conducting sentiment analysis from product reviews
Analyze value and implications reviews have for businesses and users

5. Sentiment Analysis
Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics
People express opinions in complex ways
Can be misleading

6. Sentiment Analysis cont.
Many possibilities about how to classify product reviews
Users
Sentences
Predetermined descriptive phrases (“high quality” , or “great price”)
Words (great, excellent, slow, fast, etc.)

7. Sentiment Analysis Example
The iPhone 4 boasts a glass screen that Apple claims is stronger than plastic and more resistant to scratches. But its durability--and scratch resistance--has yet to be tested by users' everyday wear. Already, there are reports that the iPhone's screen smudges easily and fails to withstand "shock and sudden impact." CNET writes in its early review of the iPhone 4, "The glass attracts smudges by the ton and durability remains a concern." iFixyouri warns the iPhone's design could make its glass screen susceptible to shattering: "On the new iPhone, the glass basically sits on top of the aluminum frame. On the old iphone, it was recessed and protected by a chrome bezel."

9. Methodology

10. Methodology 142.8 million reviews spanning from 1996 to 2014
82.83 million reviews resulting from aggressively deduplicated data
Python dictionary object, use a script to parse the data that can be converted into JSON objects
Name of products, price, related products, brand name, category, review text, overall rating, reviewer name, and etc.

12. Methodology cont. Model and uncover hidden dimensions within text
Latent Dirichlet Allocation (LDA)
Determine and maximize success of given product based based off textual reviews done by users
Trim data by looking at positive reviews with a rating value greater than or equal to 4, and negative reviews with a value less than or equal to 2
Look at reviews within a smaller time period (2-4 years)
Compute average rating of a specific category or brand

13. LDA
Given a document D, and a fixed number of K topics to discover, we can learn the topic representation of each document and the words associated to each topic
I like to eat broccoli and bananas.
I ate a banana and spinach smoothie for breakfast.
Chinchillas and kittens are cute.
My sister adopted a kitten yesterday.
Look at this cute hamster munching on a piece of broccoli.
Way of automatically discovering topics that these sentences contain. LDA represents documents as a mixture of topics and spits out words with certain probabilities

14. Related Works

15. Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text
HFT Model “Hidden Factors as Topics”
Combines ratings along with review text to make product recommendations
Genre discovery and suggest informative reviews

16. Using community detection and link prediction to improve Amazon recommendations
Explore how they can use community detection on Amazon user and item graphs to improve link prediction between reviewers and products
Jaccard’s coefficient along with Adamic/Adar and preferential attachment in growth networks; methods based on Katz measure, Hitting time, Commute time, PageRank, and Eigen Value Centrality

18. References
2015/projects_2015/Amazon_recommendations.pdf

19. Questions?