Automatic Hierarchy Discovery and Opinion Mining of Political Blogs

1
Automatic Hierarchy Discovery and
Opinion Mining of Political Blogs

• Amit Goyal
• Kristi McBurnie
• November 28, 2007

2
Outline

• Introduction
• Previous Work
• Our Approach
• Example
• Challenges and Future Work
• Milestones
• Conclusion

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Introduction

• The Web contains a wealth of opinions about
  products, politics, newsgroup posts, review
  sites, and elsewhere
• Our interest to mine opinions expressed in user
  generated content

4
Applications

• Businesses and Organizations
• Market Intelligence A huge amount of money is
  spent to find consumer sentiments and opinions
• Opinion Polls, surveys
• Individuals interested in other opinions when
• Purchasing a product
• Finding opinion on political topics
• Using a service etc.
• Smart Ads
• Place an ad when one praises a product
• Place an ad from a competitor if one criticizes a
  product
• Opinion Search
• Provide search for opinions
• Give me opinions on gmail
• Give me comparisons between gmail vs yahoomail

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Types of opinions

• Direct Opinions sentiment expressions on
  objects. E.g. policies, politicians, movies,
  products
• E.g. I find myself in support of the Senate
  Judiciary Committee, which approved legislation
  that clears the way for millions of undocumented
  workers to continue working in America and seek
  citizenship.
• Comparisons relations expressing similarities or
  differences of more than one object.
• E.g. I think Bush will beat Kerry in the
  presidential elections or The lens quality of
  Camera A is better than Camera B

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Problem Statement

• Given a object and a collection of reviews on it,
  the task is
• Identification of features
• Making hierarchy of features
• Sentiment Analysis Determining the orientation
  and strength
• Provide a visualization (summary)

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Previous Work

• Mainly focused on product and movie reviews
• Feature Extraction
• Opinion Observer (Hu and Liu, 2004)
• Opine (Popescu and Etzioni, 2005)
• Red Opal (Scaffidi, 2007)
• Hierarchical Discovery
• To be filled by kristi

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Previous Work

• Opinion Observer

• By Bing Liu and Minqing Hu
• Feature Extraction
• Identify Nouns using POS tagging
• Identify Noun phrases by Association Rule Mining
• Compactness pruning, redundancy pruning
• Opinion word extraction
• Infrequent feature identification
• 72 precision and 80 recall

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Previous Work

• OPINE

• Feature Extraction
• First, extract nouns and noun phrases, retains
  those with frequency greater than some threshold
• Evaluates each noun phrase by computing the PMI
  (point-wise mutual information) scores between
  the phrase and meronymy discriminators associated
  with the product class
• E.g. of scanner, scanner has, scanner come
  with etc. for the Scanner class
• PMI(f,d) Hits(df) / Hits(d) Hits(f)
• Then, PMI score are converted to binary features
  for a Naïve Bayes Classifier, which outputs a
  probability associated with each fact
• Compared to Hu and Liu work, 22 better precision
  and 3 lower recall

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Previous Work

• Red Opal

• 3 components
• Feature Extractor
• Product Scorer
• User Interface
• Performs better than Opinion Observer

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Previous Work

• Red Opal

• Feature Extraction
• POS tagging, takes noun and noun phrases as
  potential features
• Use lemma frequency to rank the features
• Product Scoring Score of feature f of product p
• o(r,f) is the number of occurrences of feature f
  in review r
• w(r,f) is the weight of feature f in review r

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Previous Work

• Clustering

• Conceptual clustering
• CLUSTER/2
• Places object descriptions and attributes
  together to obtain domain-dependent goals
• COBWEB
• Favours classes that maximize the information
  that can be predicted from knowledge of class
  membership
• Hierarchical clustering
• BIRCH
• Hierarchically cluster elements in a dataset
• Level of clustering quality level in the
  hierarchy

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Previous Work

• Hierarchy Discovery

• Han and Fu define formally as A sequence of
  mapping from a set of lower-level concepts to
  their higher-level correspondences
• DBLearn automatically discovered a hierarchy of
  concepts for the purpose of data mining
• Ie birthplace may have the following hierarchy
  city, province, country
• Foreman et al.
• Trains categorizers and automatically constructs
  hierarchy of categories using human trainers
• Good GUI
• Difficult for novice users and hard to optimize

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Previous Work
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Previous Work

• Hierarchy Discovery

• Sanderson and Croft
• Automatically develop hierarchy in web documents
• Organize extracted words/phrases using
  subsumption
• No clustering or training techniques
• Yang and Lee
• Hierarchies of web directories
• Text mining to discover relationships between
  documents and between words
• Cluster them into document and word maps

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Previous Work

• Sentiment Analysis

• Esuli and Sebastiani
• 3 stages
• Determine subjective/objective polarity
• Determine positive/negative polarity
• Determine strength of the positive/negative
  polarity
• Uses SentiWordNet to assign 3 scores to each word
  (objectivity, positivity, negativity)

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Previous Work

• Sentiment Analysis

• Pang and Lee
• Only subjective sections of the movie review
• Machine learning techniques
• Pair-wise relations between extracts to build an
  undirected graph
• Minimum cut
• Efficient and results in higher accuracy rates
• Agarwal and Bhattacharyya
• SVM classifier
• Determine strength of polarity of subjective
  adjectives in good vs bad classification based on
  WordNets synonymy graph
• Applied cut-based graph similar to Pang et al
• Reached accuracies of 84-95.6

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Our proposal

• Apply feature extraction and opinion mining in
  political domain
• Applications in political domain
• Automatic opinion polls
• Identification of local/global issues in
  elections
• Target campaigning in elections
• Impact of speech
• Output ltpolitician, topic, opinion, polaritygt
• Objects are politicians
• Categories are political organizations
• Topic may be policies, issues etc
• In this project, we focus mainly on feature
  extraction and their hierarchy discovery

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Our Approach

• Observations

• Two kinds of opinions
• Direct talks about single object
• Comparison talks about multiple objects
• Two kinds of information
• Facts (objective)
• Opinions (subjective)
• Sentiment Analysis can be done only on subjective
  information
• Although, features occur both categories,
  subjective sentences are noisy

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Comparison to product domain
Product Domain Political Domain
Category Product Category (e.g. Camera) Political Organizations (e.g. Democrats)
Object Product (e.g. Camera A) Leaders (e.g. Bush)
Features/Topics Properties (e.g. lens) Policies (e.g. Immigration)
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Our Approach
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Our Approach

• Perform feature extraction
• Split into objective and subjective phrases
• Hierarchy discovery on features from objective
  sentences
• Sentiment analysis on features from subjective
  sentences

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Our Approach

• Feature Extraction

• Extract the features
• Extract nouns from POS tagging
• Extract noun phrases from Association Rule Mining
• Pruning
• Rank the features based on lemma frequency
• Identify the subjectivity of all sentences
• Mine the opinion words (adjectives)
• Use key phrases dictionary (e.g. can you
  believe, I think, I recommend etc)
• Visual differences factual data is often
  represented in quotes

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Our Approach

• Hierarchy Discovery

• 3 approaches
• Subsumption
• Sanderson and Croft
• Look at every pair of terms and apply subsumption
• X subsumes Y if the documents in which Y occurs
  are a subset of the documents in which X occurs
• P(XY) 1 and P(YX) lt 1
• Clustering
• Use DBpedia and/or YAGO

X
Y
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Our Approach

• Hierarchy Discovery

• 3 approaches
• Subsumption
• Clustering
• Yang and Lee
• Cluster phrases by co-occurrance
• Using unsiupervised learning algorithm ? SOM
  networks
• Organizes phrases into a 2D map of neurons
• According to similarity of vectors
• 3 Steps
• Training process
• Assigning phrases to a neuron
• Labelling process
• Use DBpedia and/or YAGO

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Our Approach

• Hierarchy Discovery

• 3 approaches
• Subsumption
• Clustering
• Find a group of dominating clusters (neurons)
• Make these as superclusters and put neighbours
  one level down
• Repeat for lower level of hierarchy under each
  subcluster
• Use DBpedia and/or YAGO

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Our Approach

• Hierarchy Discovery

• 3 approaches
• Subsumption
• Clustering
• Use DBpedia and/or YAGO
• DBpedia provides 3 classification schemes
• Wikipedia categories
• YAGO classification
• Word Net Sysnet Links

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Our Approach

• Hierarchy Discovery

• 3 approaches
• Subsumption
• Clustering
• Use DBpedia and/or YAGO

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Our Approach

• Hierarchy Discovery

Dbpedia and YAGO

Clustering and Subsumption
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Our Approach

• Sentiment Analysis

• 2 ways to approach this
• Subjective phrases
• What does the public think about each policy
• Objective phrases
• What is the policy
• Rank parties from each policy on a scale from
  right-wing to left-wing

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Our Approach

• Sentiment Analysis

• Subjective phrases
• What does the public think the policy
• Pang and Lee
• Cut-based classification (Pang and Lee)
• Individual scores
• Association scores
• Partition Cost
• A cut (S,T) of G is a partition of its nodes into
  sets S s U S and T t U T, where s not
  contained in S and t is not contained in T.
  Its cost cost(S,T) is the sum of the weights of
  all edges crossing from S to T
• A minimum cut of G is one of minimum cost.

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Our Approach

• Sentiment Analysis

• Subjective phrases
• What does the public think about each policy
• Agarwal and Bhattacharyya
• Determine adjective strength
• Cut-based classification between
• sentences (Pang and Lee)
• Cut-based classification between
• documents
• Improved accuracy

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Our Approach

• Sentiment Analysis

• Objective phrases
• What is the policy
• Rank parties from each policy on a scale from
  right-wing to left-wing
• Definition of polarity would be left/right using
  a comparison of left-wing and right-wing
  policies/ideals
• Instead of traditional positive/negative using
  the ideal words poor and excellent

Right-wing (Conservative)
Left-wing (Liberal)
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Example

• Ideal case

• The economic cost of the war in Iraq is estimated
  to total 1.3 trillion roughly double the
  amount the White House has requested thus far,
  according to a new report by Democrats on
  Congress Joint Economic Committee. I think this
  is an absurd amount of money to be spending on
  killing people and freeing oil fields.

• Political Organization Republicans
• Politician George Bush
• Topic War in Iraq
• Sub-topic cost
• Opinion words absurd, killing, freeing
• Polarity negative

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Example

• Feature Extraction

• The economic cost of the war in Iraq is estimated
  to total 1.3 trillion roughly double the
  amount the White House has requested thus far,
  according to a new report by Democrats on
  Congress Joint Economic Committee. I think this
  is an absurd amount of money to be spending on
  killing people and freeing oil fields.

• Noun phrases economic cost, war in Iraq, amount,
  report, amount, money, people, oil fields
• Proper nouns White House, Democrats on Congress
  Joint Economic Committee
• Frequent features economic cost, war in Iraq,
  money, oil fields, White House

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Example

• Identification of Subjectivity

• The economic cost of the war in Iraq is estimated
  to total 1.3 trillion roughly double the
  amount the White House has requested thus far,
  according to a new report by Democrats on
  Congress Joint Economic Committee. I think this
  is an absurd amount of money to be spending on
  killing people and freeing oil fields.

• Opinion words think, absurd
• 1st sentence is objective, and 2nd is subjective
• Interesting features economic cost, war in Iraq

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Example

• Hierarchy Discovery step 1

• The economic cost of the war in Iraq is estimated
  to total 1.3 trillion roughly double the
  amount the White House has requested thus far,
  according to a new report by Democrats on
  Congress Joint Economic Committee. I think this
  is an absurd amount of money to be spending on
  killing people and freeing oil fields.

• Identification of category/object for proper
  nouns using DBpedia
• Category Republicans
• Object George Bush

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Example
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Example

• Hierarchy Discovery step 2

• The economic cost of the war in Iraq is estimated
  to total 1.3 trillion roughly double the
  amount the White House has requested thus far,
  according to a new report by Democrats on
  Congress Joint Economic Committee. I think this
  is an absurd amount of money to be spending on
  killing people and freeing oil fields.

• Identification of policy hierarchy using
  subsumption and clustering
• Policies are derived from interesting features
• economic cost, war in Iraq

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Example
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Example

• Sentiment Analysis

• The economic cost of the war in Iraq is estimated
  to total 1.3 trillion roughly double the
  amount the White House has requested thus far,
  according to a new report by Democrats on
  Congress Joint Economic Committee. I think this
  is an absurd amount of money to be spending on
  killing people and freeing oil fields.

• Opinion is the subjective sentence
• Polar words absurd, spending, killing, freeing
• Polarity Negative

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Challenges

• Difficult to distinguish between objective and
  subjective information
• Opinion words also occur in objective sentences
• Identification of spam blogs
• Identification of implicit features
• Mapping politician to the policy in comparison
  blogs
• Deciding on a distance measurement for clustering

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Future Work

• Implementation of algorithms
• Summarization of opinions
• Visualization
• Refinements

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Milestones

• Decide on domain
• Read previous works
• Decide on an approach that is best for the domain
• Write up an example to illustrate it
• Challenges and future work
• Presentation
• Write the paper

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Questions?
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Previous Work

• OPINE (Backup Slide)

• Overall Process

47
Previous Work

• Opinion Observer (Backup Slide)

• By Bing Liu and
• Minqing Hu

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Types of opinions

• Direct Opinions sentiment expressions on
  objects. E.g. policies, politicians, movies,
  products
• E.g. I find myself in support of the Senate
  Judiciary Committee, which approved legislation
  that clears the way for millions of undocumented
  workers to continue working in America and seek
  citizenship.
• Comparisons relations expressing similarities or
  differences of more than one object.
• E.g. I think Bush will beat Kerry in the
  presidential elections or The lens quality of
  Camera A is better than Camera B