![Motivation People are increasingly relying on online feedback mechanisms in making choices [Guernsey 2000] Online feedback mechanisms draw consumers Competitive Edge Quality currently bad](https://image.slidesharecdn.com/effectsoflargeclusterspostprune-090807203917-phpapp02/85/Online-feedback-correlation-using-clustering-5-320.jpg)
![Overview of Project Obtain large repository of customer reviews Extract features from customer reviews and orient them Create feature vectors i.e. [1,0,-1,1,1,-1 … ] from reviews and features Cluster feature vectors to find large negative clusters Analyze clusters and compare to hypothesis](https://image.slidesharecdn.com/effectsoflargeclusterspostprune-090807203917-phpapp02/85/Online-feedback-correlation-using-clustering-11-320.jpg)
Online feedback correlation using clustering
- 1. Online Feedback Correlation using Clustering Research Work Done for CS 651: Internet Algorithms
- 2. Dedicated to Tibor Horvath Whose endless pursuit of getting a PhD (imagine that) kept him from researching this topic.
- 3. Problem Statement Millions+ of reviews available Consumers read only a small number of reviews. Reviewer content not always trustworthy
- 4. Problem Statement (continued) What information from reviews is important? What can we extract from the overall set of reviews efficiently to provide more utility to consumers than is already provided?
- 5. Motivation People are increasingly relying on online feedback mechanisms in making choices [Guernsey 2000] Online feedback mechanisms draw consumers Competitive Edge Quality currently bad
- 6. Current Solutions “ Good” review placement Show small number of reviews . . . more Trustworthy?
- 8. Observations Consumers look at a product based on its overall rating Consumers read “editorial review” for content Reviews indicate can indicate common issues … Can we correlate these reviews in some meaningful way?
- 9. Observations Lead to Hypotheses! Hypothesis: Products with numerous similar negative reviews will often not be purchased regardless of their positive reviews. Furthermore, the number of negative reviews is a high indication of the likeliness of certain flaws in a product.
- 10. Definitions Semantic Orientation: polar classification of whether something is positive or negative Natural Language Processing: deciphering parts of speech from free text Feature: quality of a product that customers care about Feature Vector: vector representing a review in a d-dimensional space where each dimension represents a feature.
- 11. Overview of Project Obtain large repository of customer reviews Extract features from customer reviews and orient them Create feature vectors i.e. [1,0,-1,1,1,-1 … ] from reviews and features Cluster feature vectors to find large negative clusters Analyze clusters and compare to hypothesis
- 12. Related Work Related work has fallen into one of three disparate camps Classification: classifying Reviews into Negative or Positive reviews Domain Specificity: overall effect of reviews in a domain Summarization: features extraction to summarize reviews
- 13. Limitations of Related Work Classification Overly summarizing Domain Specificity Hard to generalize given domain information Summarization No overall knowledge of collection
- 14. Close to Summarization? Most closely related to work done in Summarization by Hu and Liu. Summarization with dynamical feature extraction and orientation per review
- 15. Data for Project Data from Amazon.com customer reviews Available through the use of Amazon E-Commerce Service (ECS) Four thousand products related to mp3 players Over twenty thousand customer reviews
- 16. Technologies Used Java to program modules Amazon ECS NLProcessor (trial version) from Infogistics Princeton’s WordNet as a thesaurus KMLocal from David Mount’s group at University of Maryland for clustering
- 18. Simplifications Made Limited data set Feature list created a priori Features from same sentence given same orientation Sentences without features neglected Number of clusters chosen only to see correlations in biggest cluster Small adjective seed set
- 19. Analysis Associated Clusters with Products Found negative clusters using threshold (-0.1) Eliminated non-Negative Clusters Sorted products list twice Products by sales rank (given by Amazon) Products sorted by hypothesis with tweak Tweak: Relative Size * Distortion Computed Spearman’s Distance
- 20. Results Hypothesis calculates with 82% accuracy! But most of the four thousand products were pruned due to poor orientation
- 21. Conclusion Consumers are affected by negative reviews that correlate to show similar flaws. Affected regardless of the positive reviews
- 22. Future Work Larger seed set for adjectives Use more complicated NLP techniques Experiment with the size of clusters Dynamically determine features using summary techniques Use different data sets Use different distance measure in clustering
- 23. Questions