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Opinion Observer: Analyzing and Comparing Opinions on the Web Bing Liu, Minqing Hu, Junsheng Cheng Paper Presentation:Vinay Goel Introduction Web: excellent source of consumer opinions Online customer reviews of products Useful information to customers and product manufacturers

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opinion observer analyzing and comparing opinions on the web

Opinion Observer: Analyzing and Comparing Opinions on the Web

Bing Liu, Minqing Hu, Junsheng Cheng

Paper Presentation:Vinay Goel

introduction
Introduction
  • Web: excellent source of consumer opinions
  • Online customer reviews of products
  • Useful information to customers and product manufacturers
  • Novel framework for analyzing and comparing customer opinions
  • Technique based on language pattern matching to extract product features
technical tasks
Technical Tasks
  • Identify product features that customers have expressed their opinions on
  • For each feature, identify whether the opinion is positive or negative
  • Review Format (2) - Pros, Cons and detailed review
  • The paper proposes a technique to identify product features from pros and cons in this format
problem statement
Problem Statement
  • Let P={P1,P2 …Pn} be a set of products that the user is interested in
  • Each product Pi has a set of reviews Ri ={r1,r2 …rk}
  • Each review rj is a sequence of sentences rj= {sj1,sj2 …sjm}
product feature
Product Feature
  • A product feature f in rj is an attribute/component of the product that has been commented on in rj
  • If f appears in rj, explicit feature
    • “The battery life of this camera is too short”
  • If f does not appear in rj but is implied, implicit feature
    • “This camera is too large” (size)
opinions and features
Opinions and features
  • Opinion segment of a feature
    • Set of consecutive sentences that expresses a positive or negative opinion on f
    • “The picture quality is good, but the battery life is short”
  • Positive opinion set of a feature (Pset)
    • Set of opinion segments of f that expresses positive opinions about f from all the reviews of the product
    • Nset can be defined similarly
automated opinion analysis
Automated opinion analysis

Explicit and implicit features

Synonyms

Granularity of features

extracting product features labeling
Extracting Product Features - Labeling
  • Perform POS tagging and remove digits
    • “<V>included<N>MB<V>is<Adj>stingy”
  • Replace actual feature words with [feature]
    • “<V>included<N>[feature]<V>is<Adj>stingy”
  • Use n-gram to produce shorter segments
    • “<V>included<N>[feature]<V>is”
    • “<N>[feature]<V>is<Adj>stingy”
  • Distinguish duplicate tags
    • “<N1>[feature]<N2>usage”
  • Perform word stemming
rule generation
Rule Generation
  • Association Rule Mining
  • Only need rules that have [feature] on the right-hand-side (<N1>,<N2> --> [feature])
  • Consider the sequence of items in the conditional part (left-hand-side) of each rule
  • Generate language patterns (<N1>[feature]<N2>)
feature refinement strategies
Feature Refinement strategies
  • There may be a more likely feature in the sentence segment but not extracted by any pattern
    • “slight hum from subwoofer when not in use”
  • Frequent-Noun
    • Only a noun replaces another noun
  • Frequent-Term
    • Any type replacement
extracting reviews from web pages
Extracting Reviews from Web Pages
  • Non trivial task
  • MDR-2
    • System finds patterns from page containing reviews
    • System uses these patterns to extract reviews from other pages of the site
experimental results17
Experimental Results
  • Amount of time saved by Semi-automatic tagging is around 45%
  • Group synonyms using WordNet (52% recall and 100% precision)
    • Does not handle context dependent synonyms
conclusion
Conclusion
  • Novel visual analysis system
  • Supervised pattern discovery method
  • Interactive correction of errors of the automatic system
  • Improve techniques, study strength of opinions