1 / 17

Review Mining for Music Digital Libraries: Phase II

THE ANDREW W. MELLON FOUNDATION. Review Mining for Music Digital Libraries: Phase II. J. Stephen Downie, Xiao Hu The International Music Information Retrieval Systems Evaluation Lab (IMIRSEL) University of Illinois at Urbana-Champaign. D1. D1. D2. D2. D3. D3. Description 1.

ratana
Download Presentation

Review Mining for Music Digital Libraries: Phase II

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. THE ANDREW W. MELLON FOUNDATION Review Mining for Music Digital Libraries: Phase II J. Stephen Downie, Xiao Hu The International Music Information Retrieval Systems Evaluation Lab (IMIRSEL) University of Illinois at Urbana-Champaign

  2. D1 D1 D2 D2 D3 D3 Description 1 Description 1 Description 1 Description 1 Description 1 Description 1 Description 1 Description 1 Background & Motivation Classify Reviews Identify User Descriptions Connect to Objects Positive Customer Reviews Phase I Phase II Future Negative Epinions.com

  3. Review mining: phase I

  4. Phase II Mining frequent descriptive patterns in positive and negative reviews sets of words used by users to express feelings/opinions

  5. Items Transactions Frequent Descriptive Pattern Mining (FDPM) • Finds patterns consisting of items that frequently occur together in individual transactions • Items =candidate descriptive words (terms) = adjectives, adverbs and verbs, no nouns • Transactions = review sentences

  6. POS tagging Frequent pattern mining Data/Text-to-Knowledge (D2K/T2K) Toolkits

  7. Findings Digging deeper and deeper to find out what makes good things good and bad things bad….

  8. Single term patterns Good = Bad?!

  9. good in a negative context Negation:“Nothing is good.” “It just doesn't sound good.” Song titles: “Good Charlotte, you make me so mad.” “Feels So Goodis dated and reprehensibly bad.” Rhetoric: “And this is a good ruiner: …” “What a waste of my good two dollars…” Faint praise: “…the only good thing… is the packaging.” Expressions: “You all have heard … the good old cliché.”

  10. Double term patterns Good  Bad?!

  11. Triple term patterns

  12. Comparison to an earlier study • Cunningham et al. "The Pain, The Pain": Modeling music information behavior and the songs we hate. In Proc. ofISMIR ’05 What is the worst song ever?

  13. Comparison to an earlier study

  14. Conclusions • Triple-term patterns necessary: Need to dig deeper to capture users’ emotional orientation/feelings toward music objects • Findings consistent with earlier work • Customer reviews are an excellent resource for studying the underlying intentions and contributing features of user-generated metadata

  15. Future work • Non-music cases Criticism mining on book and movie reviews • Other facets of music reviews Recommended usage metadata • Other feature studies Stylistics in customer reviews Naïve Bayesian feature ranking Noun pattern mining in different genres

  16. THE ANDREW W. MELLON FOUNDATION Questions? Thank you!

  17. References Han, J., Pei, J., and Yin, Y. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD 2000. 1-12.  Hu, X., Downie J.S., West K., and Ehmann A. Mining Music Reviews: Promising Preliminary Results. InProceedings of the 6th International Symposium on Music Information Retrieval. 2005, 536-539. Welge, M., et al.Data to Knowledge (D2K) An Automated Learning Group Report. NCSA, University of Illinois at Urbana-Champaign, 2003. (http://alg.ncsa.uiuc.edu)

More Related