The 2010 JDPA Sentiment Corpus for the Automotive Domain

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The 2010 JDPA Sentiment Corpus for the Automotive Domain

  1. 1. The JDPA Sentiment Corpus for the Automotive Domain Miriam Eckert, Lyndsie Clark, Nicolas Nicolov J.D. Power and Associates Jason S. Kessler Indiana University
  2. 2. Overview • 335 blog posts containing opinions about cars – 223K tokens of blog data • Goal of annotation project: – Examples of how words interact to evaluate entities – Annotations encode these interactions • Entities are invoked physical objects and their properties – Not just cars, car parts – People, locations, organizations, times
  3. 3. Excerpt from the corpus “last night was nice. sean bought me caribou and we went to my house to watch the baseball game … “… yesturday i helped me mom with brians house and then we went and looked at a kia spectra. it looked nice, but when we got up to it, i wasn't impressed ...”
  4. 4. Outline • Motivating example • Overview of annotation types – Some statistics • Potential uses of corpus • Comparison to other resources
  5. 5. John recently purchased a had agreat a disappointing stereo, and was mildly very grippy. He also considered a which, while highly had a better PERSON Honda Civic. CAR engine, CAR-PART CAR-PART stereo. CAR-PART CARPERSON BMW It CAR REFERS-TO priced CAR-FEATURE REFERS-TO
  6. 6. John recently purchased a had agreat a disappointing stereo, and was mildly very grippy. He also considered a which, while highly had a better PERSON Honda Civic. CAR engine, CAR-PART CAR-PART stereo. CAR-PART CARPERSON BMW It CAR priced CAR-FEATURE TARGET TARGET TARGET TARGET TARGET
  7. 7. John recently purchased a had agreat a disappointing stereo, and was mildly very grippy. He also considered a which, while highly had a better PERSON Honda Civic. CAR engine, CAR-PART CAR-PART stereo. CAR-PART CARPERSON BMW It CAR REFERS-TO priced CAR-FEATURE REFERS-TO PART-OF PART-OF FEATURE-OF PART-OF
  8. 8. John recently purchased a had a great a disappointing stereo, and was mildly very grippy. He also considered a which, while highly had a better PERSON Honda Civic. CAR engine, CAR-PART CAR-PART stereo. CAR-PART CARPERSON BMW It CAR priced CAR-FEATURE DIMENSION MORE LESS
  9. 9. John recently purchased a had a great a disappointing stereo, and was mildly very grippy. He also considered a which, while highly had a better PERSON Honda Civic. CAR engine, CAR-PART CAR-PART stereo. CAR-PART CARPERSON BMW It CAR REFERS-TO PART-OF PART-OF TARGET TARGET TARGET TARGET TARGET priced CAR-FEATURE FEATURE-OF DIMENSION MORE LESS Entity-level sentiment: positive Entity-level sentiment: mixedREFERS-TO TARGET
  10. 10. Outline • Motivating example • Overview of annotation types – Some statistics • Potential uses of corpus • Comparison to other resources
  11. 11. John recently purchased a Civic. It had a great engine and was priced well. John PERSON Civic It Entity annotations REFERS-TO REFERS-TO CAR engine CAR-PART • >20 semantic types from • ACE Entity Mention Detection Task • Generic automotive types priced CAR- FEATURE
  12. 12. Entity-relation annotations Entity-level sentiment: Positive • Relations between entities • Entity-level sentiment annotations • Sentiment flow between entities through relations • My car has a great engine. • Honda, known for its high standards, made my car. Civic CAR engine CAR- PART priced CAR- FEATURE PART-OF FEATURE- OF
  13. 13. Entity annotation type: statistics • Inter-annotator agreement • Among mentions 83% • Refers-to: 68% • 61K mentions in corpus and 43K entities • 103 documents annotated by around 3 annotators A1: …Kia Rio… A2: …Kia Rio… MATCH A1: …Kia Rio… A2: …Kia Rio… NOT A MATCH
  14. 14. Sentiment expressions great engine highly priced Prior polarity: positive Prior polarity: negative • Evaluations • Target mentions • Prior polarity: • Semantic orientation given target • positive, negative, neutral, mixed … a highly spec’ed Prior polarity: positive
  15. 15. Sentiment expressions • Occurrences in corpus: 10K • 13% are multi-word • like no other, get up and go • 49% are headed by adjectives • 22% nouns (damage, good amount) • 20% verbs (likes, upset) • 5% adverbs (highly)
  16. 16. Sentiment expressions • 75% of sentiment expression occurrences have non evaluative uses in corpus • “light” – …the car seemed too light to be safe… – …vehicles in the light truck category… • 77% sentiment expression occurrences are positive • Inter-annotator agreement: – 75% spans, 66% targets, 95% prior polarity
  17. 17. Modifiers -> contextual polarity NEGATORS not a good car not a very good car INTENSIFIERS very good cara kind of good cara UPWARD DOWNARD NEUTRALIZERS i f goodthe car is I hope goodthe car is COMMITTERS sure goodthe car isI am UPWARD suspect goodthe car isI DOWNWARD
  18. 18. Other annotations • Speech events (not sourced from author) –John thinks the car is good. • Comparisons: –Car X has a better engine than car Y. –Handles a variety of cases
  19. 19. Outline • Motivating example • Overview of annotation types – Some statistics • Potential uses of corpus • Comparison to other resources
  20. 20. Possible tasks • Detecting mentions, sentiment expressions, and modifiers • Identifying targets of sentiment expressions, modifiers • Coreference resolution • Finding part-of, feature-of, etc. relations • Identifying errors/inconsistencies in data
  21. 21. Possible tasks • Exploring how elements interact: – Some idiot thinks this is a good car. • Evaluating unsupervised sentiment systems or those trained on other domains • How do relations between entities transfer sentiment? – The car’s paint job is flawless but the safety record is poor. • Solution to one task may be useful in solving another.
  22. 22. But wait, there’s more! • 180 digital camera blog posts were annotated • Total of 223,001 + 108,593 = 331,594 tokens
  23. 23. Outline • Motivating example – Elements combine to render entity-level sentiment • Overview of annotation types – Some statistics • Potential uses of corpus • Comparison to other resources
  24. 24. Other resources • MPQA Version 2.0 – Wiebe, Wilson and Cardie (2005) – Largely professionally written news articles – Subjective expression • “beliefs, emotions, sentiments, speculations, etc.” – Attitude, contextual sentiment on subjective expressions – Target, source annotations – 226K tokens (JDPA: 332K)
  25. 25. Other resources • Data sets provided by Bing Liu (2004, 2008) – Customer-written consumer electronics product reviews – Contextual sentiment toward mention of product – Comparison annotations – 130K tokens (JDPA: 332K)
  26. 26. Thank you! • Obtaining the corpus: – Research and educational purposes – ICWSM.JDPA.corpus@gmail.com – June 2010 – Annotation guidelines: http://www.cs.indiana.edu/~jaskessl • Thanks to: Prof. Michael Gasser, Prof. James Martin, Prof. Martha Palmer, Prof. Michael Mozer, William Headden
  27. 27. Top 20 annotations by type
  28. 28. Inter-annotator agreement

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