This document summarizes a research paper that analyzed how characteristics of online product reviews and reviewers impact perceived review helpfulness and product sales. The paper used regression analysis to show that reviews with higher subjectivity, readability and fewer spelling errors correlated with increased sales and perceived helpfulness. It also used random forest models to predict how reviews would be rated for helpfulness and their impact on sales. The models showed user preferences for more objective reviews of feature-based goods and more subjective reviews of experience goods.

1. “Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics” Authors: AnindyaGhose, Panagiotis G. Ipeirotis, Member, IEEE Course: Topics in Data mining Presenter: NobalNiraula December 8, 2010 @ UOM 1

2. Introduction Gathering variables (Attributes) Explanatory study using Econometric Regression Hypothesis for sales Hypothesis for perceived usefulness Prediction Helpfulness Impact on sales Conclusion Outline 2

3. Product related word-of-mouth conversations in online markets Reviewers contribute time and energy Volume of review could be high Benefits Customers: Usefulness / Helpfulness Average Star Rating Bimodel Peer Review  Biased Helpfulness = helpful votes / total votes “Spotlight Review” in Amazon.com Manufacturers: Influence on Sales Helpful reviews are not necessarily the ones that lead to increases in sales ! Reviews that affect most should be presented first to manufacturers Introduction (1) 3

4. The paper is unique in looking at how subjectivity level, readabilityandspelling errors in the text of reviews affect product sales and the perceived helpfulness of these reviews. 4 Introduction (2)

5. Two Level Study Explanatory Econometric Analysis Identify aspects of a review a reviewer Prediction Model using “Random Forests” How peer consumers are going to rate a review How sales will be affected by the posted review Predicting Helpfulness and Importance 5

6. Product Reviews 6

7. Sample Review 7

8. Reviewer’s Profile 8

9. Product Rank 9

10. Variables Collection Products Audio and video players (144 products), Digital cameras (109 products), and DVDs (158 products). Product and Sales Data :Retail Price, Sales Rank, Average Rating, Number of Reviews, Elapsed Date Reviewer History: Number of Past Reviews, Reviewer History Micro, Reviewer History Micro, Past Helpful Votes, Past Total Votes Reviewer Characteristics: Reviewer Rank, Top-10 Reviewer, Top-50 Reviewer, Top-100 Reviewer, Top-500 Reviewer, Real Name, Nick Name, Hobbies, Birthday, Location, Web page, Interests, Snippet, Any Discloser Individual Review: Moderate Review, Helpful Votes, Total Votes, Helpfulness Review Readability : Length(Chars), Length (Words), Length(Sentence), Spelling Error, ARI, Gunning Index, Coleman–Liau index, Flesch Reading Ease, Flesch–Kincaid Grade Level, SMOG Review Subjectivity: AvgProb, DevProb 10

11. Readability Analysis Automated Readability Index Coleman-Liau Index Flesch-Kincaid Grade Level Gunning fog index SMOG Subjectivity Analysis Stylistic Choices : “Subjective” vs “Objective” Each document gets a “Subjectivity Score” AvgProb (r) : High value Many Subjective sentences DevProb (r) : High Value Mixed (Subj+Obj) sentences 11 Text of a Review Matters !

12. Hypothesis 1a: All else equal, a change in the subjectivity level and mixture of objective and subjective statements in reviews will be associated with a change in sales. Hypothesis 1b: All else equal, a change in the readability score of reviews will be associated with a change in sales. Hypothesis 1c: All else equal, a decrease in the proportion of spelling errors in reviews will be positively related to sales. Hypothesis for Sales 12

13. ln(D) = a + b * ln(S) D is the unobserved product demand S is its observed sales rank Pareto Distribution High sales rank  low demand Key Observation: “Sales rank” in Amazon.com can be taken as PROXY of Demand ! 13 Effect on Product Sales

14. 14 Descriptive Statistics for Econometric Analysis

17. Hypothesis 1a: High subjective sentences increase sales Mixture of subjective and objective sentences are negatively associated with product sales compared to highly subjective and objective sentences. Hypothesis 1b: Higher readability scores are associated with higher sales Hypothesis 1c An increase in proportion of spelling mistakesdecreases product sales for some “experience products” like DVDs however the proportion of spelling errors doesn’t have significant impact on sales for “search products” Reviews with that rate products negatively can be associated with increased product sales when the review text is informative and detailed !!! 17 Conclusion (1)

18. Hypothesis 2a: All else equal, a change in the subjectivity level and mixture of objective and subjective statements in a review will be associated with a change in the perceived helpfulness of that review Hypothesis 2b: All else equal, a change in the readability of a review will be associated with a change the perceived helpfulness of that review. Hypothesis 2c: All else equal, a decrease in the proportion of spelling errors in a review will be positively related to perceived helpfulness of that review. Hypothesis 2d: All else equal, an increase in the average helpfulness of a reviewer’s historical reviews will be positively related to perceived helpfulness of a review posted by that reviewer. Hypothesis for Helpfulness 18

20. Hypothesis 2a: In general, mixture of subjective and objective elements more informative (helpful) by the users. For feature-based goods users prefer reviews having more objective information and less subjective sentences For experience goods, e.g. DVD, users expect few objective sentences but more subjective sentences Hypothesis 2b – 2d : Increase in the readability of reviews has a positive and statistically impact on review helpfulness An increase in proportion of spelling errors has a negative and statistically significant impact review helpfulness for audio-video products and DVDs. Past historical information about reviewers has a statistically significant effect on the perceived helpfulness of reviews 21 Conclusion (2)

21. Main goal Is the review informative or not ? Does the review impact on sales or not ? Question: given a helpfulness value of a review, decide whether it is useful or not Helpfulness = (Helpful votes/ Total votes) Continuous to binary conversion Threshold found is 60 % Classification Regression Model can be used Binary Classification 22 Predictive Modeling

22. Classifiers SVM VS Random Forest SVM consistently performed worse unlike reported in reports Training time for SVM was significantly higher than that of Random Forest 23 Predicting Helpfulness (1)

23. 24 Predicting Helpfulness (2)

24. Examining whether the difference SalesRankt(r)+T − SalesRankt(r) where t(r) is the time the review is posted, is positive or negative. 25 Predicting Impact on Sales

25. Random Forest based prediction For experience goods such as DVDs classifier has lower performance Observed high correlation of “classification error” with “distribution of review ratings” Reviews that have received widely fluctuating ratings also have reviews with widely fluctuating helpfulness votes. Highly detailed and readable reviews can have low helpfulness votes “reviewer-related”, “review subjectivity” and “review readability” features sets are interchangeable! 26 Conclusion (3)

26. Subjectivity level, readabilityandspelling errors in the text of reviews affect product sales and the perceived helpfulness 27 Overall Conclusion

27. AnindyaGhose, Panagiotis G. Ipeirotis, "Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics," IEEE Transactions on Knowledge and Data Engineering, vol. 99, no. PrePrints, , 2010 28 References

28. Thank You ! 29