Exploring the Relationship between Customer Reviews and Prices - - PowerPoint PPT Presentation

Exploring the Relationship between Customer Reviews and Prices

Lingjie Zhang, Lin Gong, Bo Man

Roadmap

• Introduction…………………………...Lingjie
• Methodology………………………….Lin
• Experimental Results…...…………...Bo

Customer Reviews Play an Important Role

90% customers say buying decisions are influenced by online reviews.

Use of Customer Reviews

For customers

• Decision
• Recommendation

For retailers

• Feedback
• Marketing strategies

To what extend do they care about those reviews?

Motivation

Do customer reviews indirectly affect sale prices?

Related Work

Classify reviews to help make decisions. Extract opinion features in customer reviews. Recommend products for customers. None of them combine customer reviews with prices.

Challenge

• Relationship(Reviews,Prices)?
• Rating = Content?

Methodology

Step 1: Collect Reviews

SNAP Amazon reviews:

• Products with over 100 reviews, in total 419 products.
• Time period: Aug, 2012 - Mar, 2013

Step 2: Assumption

User ratings == User reviews Machine Learning Methods are adopted. (Naive Bayes, Logistics Regression, Support Vector Machine) Given contents -> predict ratings. Compare final precisions and recalls.

Prediction Results: Naive Bayes

Step 3: Crawl Prices

Price data：

• 221 items from previous 419 items
• Time period: Oct, 2012 - Mar, 2013

Scaling: Moving average: Shift Analysis:

• Compare against the prices ending L days

later than the ratings.

Correlation Analysis:

• Pearson correlation coefficient is adopted.

Step 4: Analysis

L L

Experimental Results

Sample Selection

Criteria:

Count (price changes) > 50, in 6 months

Sample size:

26 out of 221 items

Experimental Results

Scaling of prices

5 1

Experimental Results

Moving Average & Tuning Parameter (window length)

Experimental Results

Shifting Analysis of prices and ratings(score)

Experimental Results

Correlation Analysis of prices and ratings

Conclusion

• Relationship exists between prices and reviews.
• Reviews influence prices in most (⅔) of the items.
• Reviews often influence prices after 7-30 days.
• Categories with loose market forces fit this rule better.
• like Home, Sport, Baby

Future Work

• Improvement on sample selection.
• Analyze relationship between prices and reviews.
• For each separate category
• With an expansion from single correlation calculation
• Focus more on negative reviews
• Use our rules to predict prices.

References

[2] P. H. Calais Guerra, A. Veloso, W. Meira Jr, and V. Almeida. From bias to opinion: a transfer-learning approach to real-time sentiment analysis. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 150–158. ACM, 2011. [3] J. L. Elsas and N. Glance. Shopping for top forums: discovering online discussion for product research. In Proceedings of the First Workshop on Social Media Analytics, pages 23–30. ACM, 2010. [4] M. Hu and B. Liu. Mining opinion features in customer reviews. In AAAI, volume 4, pages 755–760, 2004. [5] J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on Recommender systems, pages 165–172. ACM, 2013. [6] S. M. Mudambi and D. Schuff. What makes a helpful online review? a study of customer reviews on amazon.com. Management Information Systems Quarterly, 34(1):11, 2010. [7] B. O’Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith. From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 11:122–129, 2010. [8] B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pages 79–86. Association for Computational Linguistics, 2002. [9] K. Reschke, A. Vogel, and D. Jurafsky. Generating recommendation dialogs by extracting information from user

• reviews. In ACL (2), pages 499–504, 2013.

Thank you!

UVa IR Course Project Dec 5, 2014

Backup

Review Format

product/productId: B000GKXY4S product/title: Crazy Shape Scissor Set product/price: unknown review/userId: A1QA985ULVCQOB review/profileName: Carleen M. Amadio "Lady Dragonfly" review/helpfulness: 2/2 review/score: 5.0 review/time: 1314057600 review/summary: Fun for adults too! review/text: I really enjoy these scissors for my inspiration books that I am making (like collage, but in books) and using these different textures these give is just wonderful, makes a great statement with the pictures and sayings. Want more, perfect for any need you have even for gifts as well. Pretty cool!

SNAP Amazon reviews: Products with over 100 reviews, [Aug, 2012 - Mar, 2013]

Logistics Regression Prediction Results:

Logistics Regression Prediction Results

Support Vector Machine Prediction Results:

Support Vector Machine Prediction Results