Opinion Analysis

1
Opinion Analysis

• Sudeshna Sarkar
• IIT Kharagpur

2
Introduction facts and opinions

• Two main types of information on the Web.
• Facts and Opinions
• Current search engines search for facts (assume
  they are true)
• Facts can be expressed with topic keywords.
• Search engines do not search for opinions
• Opinions are hard to express with a few keywords
• How do people think of Motorola Cell phones?
• Current search ranking strategy is not
  appropriate for opinion retrieval/search.

3
Overview

• Motivation
• Definitions
• Coarse grained vs Fine grained opinion analysis
• Opinion Lexicons
• Approaches to document level opinion analysis
• Lexicon based
• Supervised learning approaches
• Mixed approaches
• Approaches to fine-grained opinion analysis
• Rule based
• Learning
• Opinion mining work at IIT Kharagpur

4
Opinion Mining

• Search for and aggregate opinions from online
  sources
• Many reviews have both positive and negative
  sentences
• Many products are liked by some and disliked by
  others there must be different reasons
• Identify different features/ aspects of the
  target and the opinion on these separately

5
Why do opinion analysis?

• Opinion search
• to extract examples of particular types of
  positive or negative statements on some topic.
• Opinion question answering
• What is the reaction to the Left Fronts stand on
  the nuclear deal?
• Is support diminishing for the UPA government?
• Product review mining
• What features of Mr Coffee programmable coffee
  maker do users like and what they dislike
  (Microsoft Live)
• Review classification
• Tracking sentiment toward topics over time
• to track the ups and downs of aggregate attitudes
  to a brand or product

6
Introduction Applications

• Businesses and organizations product and service
  benchmarking. Market intelligence.
• Business spends a huge amount of money to find
  consumer sentiments and opinions.
• Consultants, surveys and focused groups, etc
• Individuals interested in others opinions when
• Purchasing a product or using a service,
• Finding opinions on political topics,
• Many other decision making tasks.
• Ads placements Placing ads in user-generated
  content
• Place an ad when one praises an product.
• Place an ad from a competitor if one criticizes
  an product.
• Opinion retrieval/search providing general
  search for opinions.

7
Question Answering

• Opinion question answering

Q What is the international reaction to the
reelection of Robert Mugabe as President of
Zimbabwe?
A African observers generally approved of his
victory while Western Governments denounced it.
8
Opinion search (Liu, Web Data Mining book, 2007)

• Can you search for opinions as conveniently as
  general Web search?
• Whenever you need to make a decision, you may
  want some opinions from others,
• Wouldnt it be nice? you can find them on a
  search system instantly, by issuing queries such
  as
• Opinions Motorola cell phones
• Comparisons Motorola vs. Nokia
• Cannot be done yet!

9
Typical opinion search queries

• Find the opinion of a person or organization
  (opinion holder) on a particular object or a
  feature of an object.
• E.g., what is Bill Clintons opinion on abortion?
• Find positive and/or negative opinions on a
  particular object (or some features of the
  object), e.g.,
• customer opinions on a digital camera,
• public opinions on a political topic.
• Find how opinions on an object change with time.
• How object A compares with Object B?
• Gmail vs. Yahoo mail

10
Find the opinion of a person on X

• In some cases, the general search engine can
  handle it, i.e., using suitable keywords.
• Bill Clintons opinion on abortion
• Reason
• One person or organization usually has only one
  opinion on a particular topic.
• The opinion is likely contained in a single
  document.
• Thus, a good keyword query may be sufficient.

11
Find opinions on an object X

• We use product reviews as an example
• Searching for opinions in product reviews is
  different from general Web search.
• E.g., search for opinions on Motorola RAZR V3
• General Web search for a fact rank pages
  according to some authority and relevance scores.
  
• The user views the first page (if the search is
  perfect).
• One fact Multiple facts
• Opinion search rank is desirable, however
• reading only the review ranked at the top is
  dangerous because it is only the opinion of one
  person.
• One opinion ? Multiple opinions

12
Search opinions (contd)

• Ranking
• produce two rankings
• Positive opinions and negative opinions
• Some kind of summary of both, e.g., of each
• Or, one ranking but
• The top (say 30) reviews should reflect the
  natural distribution of all reviews (assume that
  there is no spam), i.e., with the right balance
  of positive and negative reviews.
• Questions
• Should the user reads all the top reviews? OR
• Should the system prepare a summary of the
  reviews?

13
User generated content

• Word of mouth on the web.
• Review sites
• Blogs
• Online forums
• Shopping comparison sites
• User reviews
• Mine opinions expressed in the user-generated
  content
• Challenging task
• Useful to individual consumers and companies.

14
Motivation for Consumer

• I want to buy a camera.
• Which model should I pick?
• Ask my friends
• Use the internet
• CEA-CNET Study Tech-Savvy Consumers Use Internet
  to Research Products Before Buying Them
• Wireless News,  November, 2007  
• Seventy Percent of Consumers Use Internet to
  Research Consumer Packaged Goods, According to
  Prospectiv Survey
• Market Wire,  January, 2008  

15
Businesses

• Identify opinions about products help to
  position/ adapt products
• Much of product feedback is web-based
• provided by customers/critiques online through
  websites, discussion boards, mailing lists, and
  blogs, CRM Portals.
• Market research is becoming unwieldy
• Sources are heterogeneous and multilingual in
  nature

16
Facts vs Opinions

• An opinion is a person's ideas and thoughts
  towards something. It is an assessment, judgment
  or evaluation of something. An opinion is not a
  fact, because opinions are either not
  falsifiable, or the opinion has not been proven
  or verified. ...en.wikipedia.org/wiki/Opinion
• Subjectivity The linguistic expression of
  somebodys emotions, sentiments, evaluations,
  opinions, beliefs, speculations, etc.
• Polarity positive and negative
• This camera is awesome.
• The movie is too long and boring.
• Strength of opinion

17
Levels of opinion analysis

• Coarse to fine grained opinion analysis
• Document level At the document (or review) level
• Subjective vs Objective
• Sentiment classification positive, negative or
  neutral
• Sentence level, Expression level
• Task 1 identifying subjective/opinionated
  sentences (or clauses/ phrases)
• Classes objective and subjective (opinionated)
• Task 2 sentiment classification of sentences
• Classes positive, negative and neutral.
• But a document/ sentence may contain multiple
  opinions on more than one topic from one or more
  opinion holder

18
Lexicon Development

• Manual
• Semi-automatic
• Fully automatic
• Find relevant words, phrases, patterns that can
  be used to express subjectivity
• Determine the polarity of subjective expressions

19
Opinion Words

• An opinion lexicon containing lists of positive
  and negative phrases is very useful for the
  opinion mining task at different levels
• Positive beautiful, wonderful, good, amazing,
• Negative bad, poor, terrible, cost someone an
  arm and a leg
• How to compile such a list?
• Dictionary-based approaches
• Corpus-based approaches
• Supervised
• Semi-supervised
• BUT
• Some opinion words are context independent (e.g.,
  good).
• Some are context dependent (e.g., long).

20
Hand created lists

• Create lists of opinion words appropriate for the
  domain manually
• Sentiment term
• Polarity
• Strength
• These approaches, while being interesting, are
  labor intensive and can be vulnerable to error
  and high maintenance costs

21
Dictionary-based approaches

• Start from a set of seed opinion words
• Use WordNets synsets and hierarchies to acquire
  opinion words
• Use the seeds to search for synonyms and antonyms
  in WordNet (eg, Hu and Liu, 2004).

21
22
Dictionary-based approaches

• Use additional information (e.g., glosses) and
  learning from WordNet (Andreevskaia and Bergler,
  2006) (Esuti and Sebastiani, 2005).

22
23
Dictionary-based approaches

• Advantage Good to find a lot of such words
• Weakness Do not find context dependent opinion
  words, e.g., small, long, fast.

23
24
Corpus-based approaches

• Rely on syntactic rules and co-occurrence
  patterns to extract from large corpora
• Use a list of seed words
• A large domain corpus
• Machine learning
• Advantages This approach can find domain
  (corpus) dependent opinions.

24
25
How to identify subjective terms?

• Assume that contexts are coherent
• Statistical Association If words of the same
  orientation like to co-occur together, then the
  presence of one makes the other more probable
• Use statistical measures of association to
  capture this interdependence
• Assume that contexts are coherent
• Assume that alternatives are similarly subjective

26
Corpus-based approaches (contd)

• Conjunctions Conjoined adjectives usually have
  the same orientation (Hazivassiloglou and McKeown
  1997).
• E.g., This car is beautiful and
  spacious.(conjunction)
• Start with seed words
• Use conjunctions to find adjectives with similar
  orientations
• Use log-linear regression to aggregate
  information from various conjunctions
• Use hierarchical clustering on a
  graphrepresentation of adjective similarities to
  find two groups of same orientation

26
27
(No Transcript)
28
Growing contextual opinion words

• Ding, Liu, Wu
• Intra-sentence conjunction rule Opinion on both
  sides of and / two consecutive sentences tend
  to be the same
• E.g., This camera takes great pictures and has a
  long battery life.
• But with a but-like clause, the opinions
  tend to be of opposite polarity.
• Context is important
• Long battery life vs Long time to focus
• Growing
• by applying various conjunctive rules
• Verifying the results as the system sees more
  reviews by those conjunctive rules
• Only keep those opinions which the system is
  confident about, controlled by a confidence
  limit.

28
29
Semantic Orientation by Association

• Labeled semantic orientation of words
• Pwords good, nice, excellent, positive,
  fortunate, correct, superior
• Nwords bad, nasty, poor, negative,
  unfortunate, wrong, inferior.
• Various approach to calculate the semantic
  association of two words
• Pointwise Mutual Information (PMI) Church and
  Hanks 1989
• Latent Semantic Indexing (LSI) Dumais et al.
  1990
• Likelihood Ratios Dunning 1993

30
Turney 2002 Turney Littman 2003

• Determine the semantic orientation of each
  extracted phrase based on their association with
  seven positive and seven negative seed words

31
Weakly spervised learning

• Gammon Aue 2005
• Given a list of seed words (seed words 1)
• Get more seed words (seed words 2) words with
  low PMI at sentence level
• Get semantic orientation of (seed words 2) by PMI
  at document level
• Get Semantic orientation of all words by PMI with
  all seed words

32
Document level opinion analysis

• Polarity classification Classify documents
  (e.g., reviews) based on the overall sentiments
  expressed by authors,
• Approaches
• Use opinion lexicon
• Knowledge Engineering
• Supervised learning techniques
• Classifying using the Web as a corpus
• Semi-supervised

33
Knowledge Engineering

• Make use of lists of sentiment terms
• Manually create analysis components based on
  cognitive linguistic theory parser, feature
  structure representation, etc

34
Supervised polarity classifier

• Requirements A labeled database of opinion
• Download ratings from Amazon.com, epinions.com
  etc.
• Build a binary opinion classifier
• From positive and negative ratings
• Merge 1 and 2 stars to negative and 3, 4 and 5 to
  positive
• Use thresholded SVM, maximum entropy, naïve
  Bayes, etc.

35
Supervised Training

• Obtain Labeled Sentences positive, neutral,
  negative
• Extract features words, n-grams, multi word
  expressions, feature generalization Kim Hovy
  2007
• Feature values binary/ frequency
• Run Training algorithm on the features to give a
  classifier
• Optional Do feature selection (use
  log-likelihood ratio)

36
Semi-supervised approaches

• Fully supervised techniques require
• large amount of labeled data for the given domain
• Semi-supervised systems
• Use small amount of domain knowledge
• From a small set of seed words use domain corpus
  to get domain relevant opinion words as discussed
  earlier

37
Semi-supervised approach

• Gamon Aue 2005
• Obtain opinion words by semi-supervised approach
• Given a domain corpus, label data using average
  semantic orientation
• Train classifier on labeled data