Red Opal: Product-Feature Scoring from Reviews Christopher Scaffidi Kevin Bierhoff Eric Chang Mikhael Felker Herman Ng Chun Jin School of Computer Science.

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Presentation transcript:

Red Opal: Product-Feature Scoring from Reviews Christopher Scaffidi Kevin Bierhoff Eric Chang Mikhael Felker Herman Ng Chun Jin School of Computer Science Carnegie Mellon University ACM EC 2007, San Diego, CA Note: Chun Jim presented this talk, which I am reposting with her permission.

2 Motivation Searching for quality digital camera Picture quality Auto mode Battery Shutter Memory Searching by product feature: Red Opal

3 Red Opal Overview Feature Extraction Product scoring User Interface Evaluation

Feature extraction Prior work identified technical terms as mostly nouns. For a given product category, if a certain noun occurs in reviews far more frequently than in generic English text, then that word is likely to be a product feature. A Poisson distribution was previously used to extract technical terms from texts on physics & politics.

Feature extraction For each product category, 1. Retrieve Amazon reviews for products in this category 2. Tag part-of-speech to text (e.g. “games”  NN/“game”) 3. Compute the count of each noun and compound noun 4. Compute their probability 5. Sort the most common nouns and compound nouns together according to probability, yielding the feature list

6 Product scoring for features Input: Product category, feature of interest Output: Score (1-5) Scores are specific to feature ProductMemoryPicture quality … CameraA CameraB3.74.1… CameraC CameraD4.33.9…

7 Compute scores and confidences based on review ratings Red Opal scoring algorithm Based on reviewer-assigned ratings Weighted average rating of reviews that mention feature Weigh reviews higher if feature mentioned more often Assumption: Review ratings consistent with features discussed Future work to identify the consistent rating with a feature Compute confidence in scores Measures uniformity of relevant reviewer ratings

8 User Interface: Search Screen

9 User Interface: Search Results 3 min(RedOpal) vs 10-15min(general)

10 Evaluation: Feature Extraction Time complexity: O(n), 0.9s per review.

11 Evaluation: Scoring Precision

12 Summary Red Opal: searching on-line catalog by feature Future Work: Integrating manufacturer product description Finding opinion words User-specified features Multi-feature search

13 Acknowledgements Norman M. Sadeh, Mary Shaw, Jonathan Aldrich, and Jaime Carbonell Fall 2005 “Web Commerce, Security and Privacy” class (questionnaire subjects)

14 Thank you! Questions and Comments?