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Arpit Maheshwari
Pankhil Chheda
Pratik Desai
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Contents1. Introduction And Basic Definitions
2. Applications
3. Challenges
4. Problem Formulation and Key Concepts
5. Popular Approaches
5.1 Polarity Classification: ML techniques
5.2 Subjectivity Detection: Learning Extractions
5.3 Sentiment Analysis: using minimum cuts for subjectivity
5.4 Sentiment Analysis: a new approach
6. Publicly Available Resources
7.References
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1. Introduction and Basic DefinitionsSentiment analysis : Determining the
attitude / opinion/perspective of an author on a particular subject
Different from other NLP tasks (emphasized in part 3: challenges)
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ImportanceConsumer reviews play an important role in
determining our attitude towards something unknown e.g. Product reviews, restaurant reviews etc.
Need/curiosity to know the prevalent point-of-view e.g.- to know which party is favorable to win in coming elections
Companies anxious to understand how their products and services are perceived
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Key termsSentiment/opinionSubjectivitySubjectivity analysisSentiment analysis/Opinion Mining : taken
broadly to mean the computational treatment of opinion, sentiment, and subjectivity in text
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2. Applications2.1 Applications to Review-Related
Websites
2.2 Applications in Business
2.3 Applications Across Different Domains
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3.General ChallengesComparision with a similar-looking NLP task:Topic-based categorization
Appears easier on the first lookComing up with the right set of keywords
might be less trivial than one might initially think
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General Challenges contd..Applying machine learning techniques based
on unigram models can achieve over 80% in accuracy, which is much better than the performance based on hand-picked keywords, roughly 60%
Compared to topic, sentiment can often be expressed in a more subtle manner
Subjectivity is an innate problem for us in Sentiment Analysis
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Contd..Somewhat in contrast with topic-based text
categorization, order effects can completely overwhelm frequency effects
e.g.- 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.
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4. Problem formulation and key concepts
Classification : Given a piece of opinionated text, determine the opinion/mood/sentiment from a set of values (binary e.g. positive/negative or on a scale)
Text Summarization: Reducing the length of text (subjective and/or objective) keeping the useful pieces of information/opinions
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We would cover the problem of classification in greater detail
After covering the issues in brief, we turn to certain popular approaches as found in literature
Finally summarize the approaches mentioned followed by a list of publicly available resources relevant to Sentiment Analysis
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SubjectivityIssue: The input text need not be completely
opinionatedSubjective vs Objective textIs the distinction clear? e.g.- Consider the difference between “the
battery lasts 2 hours” vs. “the battery only lasts 2 hours”
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Subjectivity contd..Problem: It has been found that the
subjectivity classification problem is itself more difficult than the Polarity classification problem
An improvement in the former => improvement in latter (Why so?)
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5. Popular Approaches
5.1 Polarity Classification: ML techniques
5.2 Subjectivity Detection: Learning
Extractions
5.3 Sentiment Analysis: using minimum cuts
for subjectivity
5.4 Sentiment Analysis: a new approach
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5.1 Polarity Classification: ML techniques
sentiments can be expressed more subtly as compd to expression of topic which is detectable by keywords
3 machine learning techniques for the task (common to many NLP tasks):
1. Naïve Bayes 2. Maximum entropy3. SVM (Support Vector Machine)
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Common frameworkBAG-OF-FEATURES: employed for all 3
methods{f1, f2, f3….fm} : predefined set of m features
that can appear in a documente.g. word “stinks”, bigram “hats off” D= {n1(d), n2(d), n3(d)….nm(d) }: no. of times
the document d contains the featuresThe vector D represents the document d
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Naïve Bayes Approachc : category d: document to be classifiedTo find c*=argmax{c} (P(c|d))P(c|d) = P(c)P(d|c)/P(d) :Bayes RuleAssumption: fi`s are conditionally
independent given d`s classP(c|d) = P(c)*P(fi|c)ni(d)/P(d)
Training method: estimation of P(c) and P(fi|c)
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Maximum Entropy ModelP(c|d) = exp() / Z(d)Z(d) : normalization constantFeature Class Function: = 1 if ni(d)>0
= 0 elseFeature Weight parameters: For details of a generic Maximum Entropy
model use in NLP, refer Berger et al, 1996
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SVM
• Large margin Classifiers
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Results: (Pang et al, 2002)
Note: baseline results ranged from 50% to 69%.
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A Conclusion:Accuracy achieved by ML methods in
Sentiment Analysis is less than topic-based categorization
Need to look for novel approachesA common phenomenon in the documents
was “thwarted expressions” narrative: author sets up a deliberate contrast to earlier discussion
“The whole is not necessarily the sum of parts” – Turney, 2002
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5.2 Subjectivity Detection: Learning Extractions
Earlier resources contain lists of subjective words
However, subjective language can be exhibited by a staggering variety of words and phrases
Subjectivity learning systems must be trained on extremely large text collections
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Riloff and Wiebe, 2003 Two salient points:1. Exploring the use of bootstrapping
methods to allow subjectivity classifiers to learn from a collection of unannotated texts
2. Using extraction patterns to represent subjective expressions. These patterns are linguistically richer and more flexible than single words or N-grams
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Extraction PatternsConsider a subjective sentence like “His kid
always drives me up the wall”Possible abstraction: <x> drives <y> up the
wallThis extraction pattern, so formed
contributes to the sentence being subjectiveOther examples:
<x> agree with <y> <x> is out of his mind
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Schematic representation
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High-precision subjectivity Classifiersuse lists of lexical items that have been shown
in previous work to be good subjectivity cluesThe subjectivity clues are divided into those
that are strongly subjective and those that are weakly subjective
The high-precision subjective classifier classifies a sentence as subjective if it contains two or more of the strongly subjective clues. On a manually annotated test set, this classifier achieves 91.5% precision and 31.9% recall
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Contd..high-precision objective classifier
classifies a sentence as objective if there are no strongly subjective clues and at most one weakly subjective clue in the current, previous, and next sentence combined (82.6% precision and 16.4% recall)
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Learning Subjective Extraction Patterns
Choose extraction patterns for which freq(pattern)>T1 and Pr(subjective|pattern) >T2T1, T2 : threshold values
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Evaluation of learned Patterns
precision ranges from 71% - 85%Hence, the extraction patterns so learned are effective at recognizing subjective expressions
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Evaluation of the Bootstrapping processThe extraction patterns so learned do form a
subjectivity detector, these can also be used to enhance the high-precision subjectivity classifier
When incorporated, the following results were observed
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5.3 Sentiment Analysis: minimum cuts
Subjectivity Summarization Based on Minimum Cuts
Basic Strategy:1. Label the sentences in the document as either
subjective or objective, discarding the latter2. Apply a standard machine-learning classifier
to the resulting extract
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Schematic representation
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Context and Subjectivity DetectionEarlier subjectivity detectors considered
sentences in isolationContext Dependency: Nearby statements
shall receive similar subjectivity tagImplemented in an elegant fashion by a
graphical concept of minimum cut
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Cut-based ClassificationTwo types of information:Individual scores indj(xi): non-negative
estimates of each xi’s preference for being in Cj based on just the features of xi alone
Association scores assoc(xi, xk): non-negative estimates of how important it is that xi and xk be in the same class
Optimization problem: Minimize the partition cost
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Graphical Formulation
Problem: Seems intractable owing to the exponential number of subsets possibleOne can use maximum-flow algorithms with polynomial asymptotic running times to exactly compute the minimum-cost cut
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Possible choices for the Association score:assoc(si, sj) = c.f(j-i) if (j-i)<T {a threshold}
= 0 elsef is a decreasing function e.g. f(d) = 1, f(d) =
e1-d, f(d) = 1/d2 etcChoices for Individual Score:Default Polarity Classifier (discussed earlier)
with training dataset as subjective + objective sentences
Our suggestion: Using extraction pattern based subjectivity detector as discussed
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Why subjective sentences matter the most
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1. How extract scores over a full review2. Why context matters in subjectivity detection too
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5.4 A new approach: A. agarwal, P. Bhattacharya
Two salient points:1. Using Wordnet synonymy graphs to
determine the weight of an adjective2. Using the technique of minimum-cut to
exploit the relationship/similarity between documents
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SVM employed as polarity classifierEvaluative strength of an adjective determined
using wordnet synonymy graph
d(wi,wj) = distance between the two words on synonymy graphs
Values in range [-1,1]These weights are used in place of the standard
binary values in feature vectors of SVM
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Similarity between sentences` subjectivity status was exploited with association score- assoc(si, sj)
Similarity also exists between documents to receive the same polarity
Assign a Mutual Similarity Co-efficient to documents as
Where fk : kth feature
Fi(fk): function that takes the value 1 if the kth feature
is present in the ith document and is 0 otherwise
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smax : largest value of the number of common features between any two documents
smin :smallest value of the number of common features
between any two documentsNo more classification of documents in
isolationMinimum-cut technique applied using MSC
and individual score of a document
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Schematic Representation
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Conclusive Summary
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Publicly Available Resources An Annotated List of Datasets Blog06 Congressional floor-debate transcripts URL:http://www.cs.cornell.edu/home/llee/data/convote.htm Cornell movie-review datasets URL: http://www.cs.cornell.edu/people/pabo/movie-review-data/ Customer review datasets URL:http://www.cs.uic.edu/∼liub/FBS/CustomerReviewData.zip Economining URL: http://economining.stern.nyu.edu/datasets.html French sentences URL: http://www.psor.ucl.ac.be/personal/yb/Resource.html MPQA Corpus URL: http://www.cs.pitt.edu/mpqa/databaserelease/ Multiple-aspect restaurant reviews URL: http://people.csail.mit.edu/bsnyder/naacl07
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Contd.. Multi-Domain Sentiment DatasetURL:http://www.cis.upenn.edu/∼mdredze/datasets/sentiment/ Review-search results sets URL: http://www.cs.cornell.edu/home/llee/data/search-subj.html List of other useful resources- General Inquirer URL: http://www.wjh.harvard.edu/∼inquirer/ NTU Sentiment Dictionary [registration required] http://nlg18.csie.ntu.edu.tw:8080/opinion/userform.js OpinionFinder’s Subjectivity Lexicon URL: http://www.cs.pitt.edu/mpqa/ SentiWordnet URL: http://sentiwordnet.isti.cnr.it/ Taboada and Grieve’s Turney adjective list [available through the Yahoo! sentimentAI group]
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References: B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment
classification using machine learning techniques,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86, 2002.
E. Riloff and J. Wiebe, “Learning extraction patterns for subjective expressions,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2003.
B. Pang and L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts,” in Proceedings of the Association for Computational Linguistics (ACL), pp. 271–278, 2004.
Alekh Agarwal and Pushpak Bhattacharyya, Sentiment Analysis: A New Approach for Effective Use of Linguistic Knowledge and Exploiting Similarities in a Set of Documents to be Classified, International Conference on Natural Language Processing ( ICON 05), IIT Kanpur, India, December, 2005.