This document summarizes an analysis of Amazon reviews from 2000-2012. It discusses the source and scope of review data, key variables of interest like review length and helpfulness. It also discusses literature on negativity bias and how negative reviews may be perceived as more diagnostic. The document explores understanding the dependent variable of review helpfulness and how it relates to factors like rating, length, and time. It proposes methods to detect expert versus novice reviewers and identify potential gender differences in reviews. It performs a sanity check of emotion analysis methods.

1. OnReviews
Analysis of Amazon Reviews (2000--2012)
Singh/Yang
New York University
D3M

2. Source & Scope of the Data
Data Source:
J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. RecSys, 2013.

4. Book Reviews
Analysis of Amazon Reviews (2000--2012)

6. Key Variables of Interest
Summary e.g. with Top 24 Tiles

7. Summary
Length & Valence of Text

8. Helpfulness of Review

9. Psych Literature on Negativity Bias
• “There is a general bias, based on both innate
predispositions and experience, in animals and
humans, to give greater weight to negative
entities (e.g., events, objects, personal traits)”
Rozin & Royzman (2011)
• Negative assessments are perceived as more
diagnostic, particularly when the assessment is
well-reasoned and elaborated at some length

10. Understanding the Dependent Variable

11. Understanding the Dependent Variable

12. Understanding the Dependent Variable

13. Understanding the Dependent Variable

14. % Helpful increasing monotonically with Rating

15. % Helpful by Review Length & Star Rating

16. % Helpful & Time Since Release

17. How do we detect Expert vs. Novice

18. Crude Measures
 How many books have you reviewed?
 How many different “categories” of books do
you review?
 How many products (e.g. books, music,
electronics) at Amazon do you review?
 Other?

19. Example: Expert if reviewed “N” books

20. Example: Expert if reviewed “N” books

21. Example: Expert if reviewed “N” books

22. Example: Expert if reviewed “N” books

23. How do we Identify Gender Differences?

24. Potential approaches
 Pick Appropriate Categories
E.g. Men vs. Women Clothing
 Scrape data on reviewer profile (+more)
In Progress
 Suggestions?

25. Category: Clothing & Accessories
Review Differences b/w Men vs. Women Clothing

26. Positive Reviews more Helpful

27. Men express more when Negative?

28. Capturing Emotions

29. Sanity Check
Do emotions correspond with basic intuition (& other methods)

30. Sanity Check
Do emotions correspond with basic intuition (& other methods)

31. Sanity Check
Do emotions correspond with basic intuition (& other methods)

32. Sanity Check
Do emotions correspond with basic intuition (& other methods)

33. Sanity Check
Do emotions correspond with basic intuition (& other methods)

34. Sanity Check
Do emotions correspond with basic intuition (& other methods)

35. Sanity Check
Do emotions correspond with basic intuition (& other methods)

36. Sanity Check
Do emotions correspond with basic intuition (& other methods)