1. An Overview of Concepts and Selected Techniques
Sentiment Analysis
An Overview of Concepts and Selected Techniques

2. Terms Sentiment Sentiment Analysis
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
1. Subjective vs objective information
2. Essentially the same as other information retrieval tasks, but with some additional challenges as we will see

3. Motivation Consumer information Marketing Politics Social
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
Review info from blogs, newsgroups, etc
Consumer attitudes towards
-company’s products
-competitor’s products
Politics
-can form basis of policy decisions

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
Lead in: these problems are similar to other IR tasks
Have a body of text---
need to know how to classify it
GRANULARITY
--Most research has used unigrams (single words)
--some research shows that k-length n-grams work best
Wordnet:
Contains large lexicon with relationships
Synonymy, antonymy, etc
Syntactic patterns
Indirect negation
Setup/contradiction

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
“[it] avoids all cliches and predictability found in Hollywood movies”
“avoids” reverses polarity of “cliches” and “predictability”
Thwarted expectation:
“This film should be brilliant. It sounds like a great plot, the actors are first grade, and the supporting cast is good as well, and Stallone is attempting to deliver a good performance. However, it can't hold up"
“unpredictable”: good for movie plot, bad for car steering

6. Approaches Machine learning Unsupervised methods 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
Machine learning
Strengths:
-perform fairly well within a given domain with sufficient training data
Weaknesses:
--in a given domain tends to overfit training data; hard to transfer learning to other domains
--need training data
Unsupervised
Strengths
--domain independent; prior polarity
--may aid machine learning techniques
weaknesses:
--when used alone, does not perform as well as machine learning w/in a given domain

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.
Document with three sentences: Y, M, N – nodes in the graph
Assign weights for each node’s (sentence’s) preference for being in each of two classes (positive or negative)
Assign weights for each node’s (sentence’s) preference for being in the same class as adjacent nodes.

10. LingPipe Polarity Classifier
Accurate as baseline but uses only 22% of content in test data (average)
Metrics suggests properties of movie review structure
Also shows performance of different classifiers

11. SentiWordNet Based on WordNet “synsets” Ternary classifier
Ternary classifier
Positive, negative, and neutral scores for each synset
Provides means of gauging sentiment for a text
Wordnet: lexical resource developed at princeton
A Synset represents a distinct semantic concept
--contains a set of synonymous words

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 Objective<1.0
Many terms are classified with some degree of subjectivity
10.45% with Objective<=0.5
0.56% with Objective<=0.125
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. 79–86, 2002.
Esuli A, Sebastiani F. SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining. In: Proc of LREC th 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.