Opinion Observer: Analyzing and Comparing Opinions on the Web

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Opinion Observer Analyzing and Comparing
Opinions on the Web

• Bing Liu, Minqing Hu, Junsheng Cheng
• Paper PresentationVinay Goel

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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

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Opinion Observer
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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

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Problem Statement

• Let PP1,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

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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)

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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

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Visualizing Opinion Comparison
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Automated opinion analysis
Explicit and implicit features Synonyms Granularit
y of features
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Extracting Product Features - Labeling

• Perform POS tagging and remove digits
• ltVgtincludedltNgtMBltVgtisltAdjgtstingy
• Replace actual feature words with feature
• ltVgtincludedltNgtfeatureltVgtisltAdjgtstingy
• Use n-gram to produce shorter segments
• ltVgtincludedltNgtfeatureltVgtis
• ltNgtfeatureltVgtisltAdjgtstingy
• Distinguish duplicate tags
• ltN1gtfeatureltN2gtusage
• Perform word stemming

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Rule Generation

• Association Rule Mining
• Only need rules that have feature on the
  right-hand-side (ltN1gt,ltN2gt --gt feature)
• Consider the sequence of items in the conditional
  part (left-hand-side) of each rule
• Generate language patterns (ltN1gtfeatureltN2gt)

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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

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Semi-Automated Tagging of Reviews
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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

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System Architecture
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Experimental Results
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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

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Conclusion

• Novel visual analysis system
• Supervised pattern discovery method
• Interactive correction of errors of the automatic
  system
• Improve techniques, study strength of opinions