Sentiment Analysis

1
Sentiment Analysis

• An Overview of Concepts and Selected Techniques

2
Terms

• Sentiment
• A thought, view, or attitude, especially one
  based mainly on emotion instead of reason
• Sentiment Analysis
• aka opinion mining
• use of natural language processing (NLP) and
  computational techniques to automate the
  extraction or classification of sentiment from
  typically unstructured text

3
Motivation

• Consumer information
• Product reviews
• Marketing
• Consumer attitudes
• Trends
• Politics
• Politicians want to know voters views
• Voters want to know policitians stances and who
  else supports them
• Social
• Find like-minded individuals or communities

4
Problem

• Which features to use?
• Words (unigrams)
• Phrases/n-grams
• Sentences
• How to interpret features for sentiment
  detection?
• Bag of words (IR)
• Annotated lexicons (WordNet, SentiWordNet)
• Syntactic patterns
• Paragraph structure

5
Challenges

• Harder than topical classification, with which
  bag of words features perform well
• Must consider other features due to
• Subtlety of sentiment expression
• irony
• expression of sentiment using neutral words
• Domain/context dependence
• words/phrases can mean different things in
  different contexts and domains
• Effect of syntax on semantics

6
Approaches

• Machine learning
• Naïve Bayes
• Maximum Entropy Classifier
• SVM
• Markov Blanket Classifier
• Accounts for conditional feature dependencies
• Allowed reduction of discriminating features from
  thousands of words to about 20 (movie review
  domain)
• Unsupervised methods
• Use lexicons

Assume pairwise independent features
7
LingPipe Polarity Classifier

• First eliminate objective sentences, then use
  remaining sentences to classify document polarity
  (reduce noise)

8
LingPipe Polarity Classifier

• Uses unigram features extracted from movie review
  data
• Assumes that adjacent sentences are likely to
  have similar subjective-objective (SO) polarity
• Uses a min-cut algorithm to efficiently extract
  subjective sentences

9
LingPipe Polarity Classifier
Graph for classifying three items.
10
LingPipe Polarity Classifier

• Accurate as baseline but uses only 22 of content
  in test data (average)
• Metrics suggests properties of movie review
  structure

11
SentiWordNet

• Based on WordNet synsets
• http//wordnet.princeton.edu/
• Ternary classifier
• Positive, negative, and neutral scores for each
  synset
• Provides means of gauging sentiment for a text

12
SentiWordNet Construction

• Created training sets of synsets, Lp and Ln
• Start with small number of synsets with
  fundamentally positive or negative semantics,
  e.g., nice and nasty
• Use WordNet relations, e.g., direct antonymy,
  similarity, derived-from, to expand Lp and Ln
  over K iterations
• Lo (objective) is set of synsets not in Lp or Ln
• Trained classifiers on training set
• Rocchio and SVM
• Use four values of K to create eight classifiers
  with different precision/recall characteristics
• As K increases, P decreases and R increases

13
SentiWordNet Results

• 24.6 synsets with Objectivelt1.0
• Many terms are classified with some degree of
  subjectivity
• 10.45 with Objectivelt0.5
• 0.56 with Objectivelt0.125
• Only a few terms are classified as definitively
  subjective
• Difficult (if not impossible) to accurately
  assess performance

14
SentiWordNet How to use it

• Use score to select features (/-)
• e.g. Zhang and Zhang (2006) used words in corpus
  with subjectivity score of 0.5 or greater
• Combine pos/neg/objective scores to calculate
  document-level score
• e.g. Devitt and Ahmad (2007) conflated polarity
  scores with a Wordnet-based graph representation
  of documents to create predictive metrics

15
References

• http//www.answers.com/sentiment, 9/22/08
• B. Pang, L. Lee, and S. Vaithyanathan, Thumbs
  up? Sentiment classification using machine
  learning techniques, in Proc Conf on Empirical
  Methods in Natural Language Processing (EMNLP),
  pp. 7986, 2002.
• Esuli A, Sebastiani F. SentiWordNet A Publicly
  Available Lexical Resource for Opinion Mining.
  In Proc of LREC 2006 - 5th Conf on Language
  Resources and Evaluation, 2006.
• Zhang E, Zhang Y. UCSC on TREC 2006 Blog Opinion
  Mining. TREC 2006 Blog Track, Opinion Retrieval
  Task.
• Devitt A, Ahmad K. Sentiment Polarity
  Identification in Financial News A
  Cohesion-based Approach. ACL 2007.
• Bo Pang , Lillian Lee, A sentimental education
  sentiment analysis using subjectivity
  summarization based on minimum cuts, Proceedings
  of the 42nd Annual Meeting on Association for
  Computational Linguistics, p.271-es, July 21-26,
  2004.