1. A Survey of Opinion Mining
Dongjoo Lee
Intelligent Database Systems Lab.
Dept. of Computer Science and Engineering
Seoul National University
Good Morning.
My name is Dongjoo Lee.
I am a member of the Intelligent Database Systems Lab.
I am here to present a survey of opinion mining.

2. Introduction Related Areas
The Web contains a wealth of opinions about products, politics, and more in newsgroup posts, review sites, and other web sites
A few problems
What is the general opinion on the proposed tax reform?
How is popular opinion on the presidential candidates evolving?
Which of our customers are unsatisfied? Why?
Opinion Mining (OM)
a recent discipline at the crossroads of information retrieval and computational linguistics which is concerned not with the subject of a document, but with opinion it expresses
Related Areas
Data Mining(DM), Information Retrieval (IR), Text Classification (TC), Text Summarization (TS)
The web contains a wealth of opinions about products, politics, and more in newsgroup posts, review sites, and elsewhere.
Many researchers and businesses try to obtain useful information from these opinion resources.
These are examples of problems to be solved using opinion resources.
Noticeably, many research papers deal with obtaining customer opinion.
Opinion mining is a recent discipline that tries to solve these problems.
And it is at the crossroads of information retrieval and computational linguistics.
It is concerned with the opinion a document expresses, not with the subject of a document.
Many techniques used in related areas, such as IR, data mining, text classification and text summarization have been used to solve OM problems.
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3. Agenda Introduction Development of Linguistic Resource
Conjunction Method
PMI Method
WordNet Expanding Method
Gloss Use Method
Sentiment Classification
Machine Learning Method
NLP Combined Method
Extracting and Summarizing Opinion Expression
Statistical Approach
NLP Based Approach
Discussion
I divided my presentation into three sections.
At first, I’ll introduce development methods of linguistic resource.
Next I’ll introduce sentiment classification methods.
Then I’ll introduce several systems for extracting and summarizing opinion expression.
At last, my discussion will be presented.
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4. Development of Linguistic Resource (1)
Linguistic resources can be used to extract opinion and to classify the sentiment of text
Appraisal Theory
Sentiment related properties are well-defined
A framework of linguistic resources which describes how writers and speakers express inter-subjective and ideological position
underlying linguistic foundation of OM
Tasks
Determining the subjectivity of a term
Determining term orientation
Determining the strength of term attitude
Example
Objective: vertical, yellow, liquid
Subjective
Positive: good < excellent
Negative: bad < terrible
Linguistic resources can be used to extract opinion and to classify sentiment.
Sentiment related properties are well-defined in Appraisal Theory.
Appraisal theory is a framework of linguistic resources which describes how writers and speakers use words.
Tasks for developing linguistic resource are as follows.
Determining the subjectivity of a term.
Determining term orientation.
Determining the strength of term attitude.
These are objective terms and these are subjective terms.
And excellent and terrible are more intense than good and bad.
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5. Development of Linguistic Resource (2)
Conjunction Method
PMI Method
Orientation
Subjectivity
WordNet Expansion Method
Gloss Use Method
SentiWordNet
I investigated four methods for developing linguistic resources.
Conjunction method and WordNet expansion method were used only for determining term orientation.
And PMI method and gloss use method were used for determining term subjectivity and orientation.
Gloss use method was also used for determining attitude strength.
As a result of Gloss use method, SentiWordNet was constructed.
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6. Conjunction Method - overview
Hatzivassiloglou and McKeown, 1997
Hypothesis
Adjectives in ‘and’ conjunctions usually have similar orientation, while ‘but’ is used with opposite orientation.
Process
Randomly selected adjectives with positive and negative orientation seed terms were used to predict orientation.
negative
All conjunction of adjectives are extracted from the corpus.
A log-linear regression model combines information from different conjunctions to determine if each two conjoined adjectives are of same or different orientation.
A clustering algorithm separates the adjectives into two subsets of different orientation. It places as many words of same orientation as possible into the same subset.
The average frequencies in each group are compared and the group with the higher frequency is labeled as positive.
positive
seed terms
corpus
and
but
Hatzivassiloglou(하찌바씰로그로) and McKeown(맥커은) used term conjunction to determine the orientation of adjectives
based on the hypothesis that,
adjectives in ‘and’ conjunctions usually have similar orientation, while ‘but’ is used with opposite orientation.
Orientation of terms is determined as follows.
At first, all conjunctions of adjectives are extracted from the corpus.
And al log-linear regression model combines information from different conjunctions to determine if each two conjoined adjectives are of same or different orientation.
Then a clustering algorithm separates the adjectives into two subsets of different orientation.
It places as many words of same orientation as possible into the same subset.
At last, the average frequencies in each group are compared and the group with the higher frequency is labeled as positive.
They used randomly selected adjectives as seed terms.
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7. Conjunction Method – objective function and constraints
Select pmin that minimizes Φ(p)
dissimilarity between adjectives in same cluster is minimized and dissimilarity between adjectives in different cluster is maximized.
Experiments
HM term set : 1,336 adjectives
657 positive, 679 negative terms
Methods to improve performance of orientation prediction
But rule : Most conjunctions had same orientation, while some conjunctions linked by ‘but’ had almost opposite orientation
log-linear regression model
morphological relationship
adequate-inadequate or thoughtful –thoughtless
log-linear model with morphological relationship : 82.5% accuracy
|Ci| : the cardinality of cluster i
d(x, y) : the dissimilarity between adjectives x , y
When clustering the adjectives, objective is to select partition p that minimizes this objective function.
This means that dissimilarity among adjectives in same cluster is minimized and dissimilarity among adjectives in different cluster is maximized.
For experiments, HM term set was created
The authors experimented with these three methods to improve accuracy of orientation prediction.
When using log-linear model with morphological relationship, they obtained the best overall accuracy.
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8. PMI Method - overview Pointwise Mutual Information (PMI)
a measure of association used in information theory and statistics
Orientation
Turney and Littman, 2003
terms with similar orientation tend to co-occur in documents
Subjectivity
Baroni and Vegnaduzzo, 2004
subjective adjectives tend to occur in the near of other subjective adjectives
Pointwise Mutual Information is a measure of association.
It is widely used in information theory and statistics.
PMI between two words is calculated through this equation.
Turney and Littman used PMI to determine term orientation based on the hypothesis
that terms with similar orientation tend to co-occur in documents.
And Baroni and Vegnaduzzo used PMI to determine term subjectivity based on the hypothesis that subjective adjectives tend to occur in the near of other subjective adjectives.
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9. PMI Method – predicting semantic orientation
Modified PMI was measured using the number of results returned by the AltaVista search engine with NEAR operator
Predicting semantic orientation of a term SO(t)
Experiments
With HM term set and three corpora
With small corpus, accuracy isn’t higher than conjunction method.
With large corpus, accuracy is higher than conjunction method.
t : target term
ti : paradigmatic term
Corpus
AV-ENG
AV-CA
TASA
Approx. # of word in corpus
1 *1011
2*109
1*107
Accuracy
87.13%
80.31%
61.83%
Turney and Littman measured modified PMI using the number of results returned by the AltaVista search engine with NEAR operator.
Using modified PMI with positive and negative seed term sets,
they determined the orientation of a term.
If SO value of a term t is bigger than zero, it is a positive term, while if it is less than zero, it is a negative term.
They experimented with HM term set and three corpora that have different total word size.
Even with a small corpus, accuracy isn’t higher than conjunction method,
However with a large corpus, accuracy is higher than conjunction method.
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10. WordNet Expansion Method
Hu et al., 2004
used synonym and antonym relationship between words
Hypothesis
adjectives usually share the same orientation as their synonyms and opposite orientation as their antonyms
By using a set of seed adjectives, orientation of all adjectives in WordNet can be assigned through a procedure exploring on the cluster graphs.
Hu et al. used synonym and antonym relationship between words in the WordNet to determine orientation of terms.
They assumes that adjectives usually share the same orientation as their synonyms and opposite orientation as their antonyms.
By using a set of seed adjectives,
orientation of all adjectives in WordNet can be assigned through a procedure exploring on the cluster graphs.
This method was used at their opinion analysis system.
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11. Gloss Use Method - overview
Esuli et al., 2005, 2006
Hypothesis
Orientation
terms with similar orientation have similar glosses
Subjectivity
terms without orientation have non-oriented glosses
SentiWordNet
All words in the WordNet have three scores
positivity, negativity, and objectivity
Term Sense is positioned in reversed triangle
good: that which is pleasing or valuable or useful; agreeable or pleasing
beautiful: aesthetically pleasing
pretty: pleasing by delicacy or grace; not imposing
yellow: similar to the color of an egg yolk
vertical: at right angles to the plane of the horizon or a base line
Esuli et al. used term glosses to determine orientation and subjectivity of a term.
They determined term orientation based on the hypothesis that terms with similar orientation have similar glosses.
And they determined term subjectivity based on this hypothesis and
another hypothesis that terms without orientation have non-oriented glosses.
At last, they constructed the SentiWordNet in which all words in the WordNet have three scores: positivity, negativity and objectivity.
Term sense is positioned in reversed triangle like this figure.
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12. Gloss Use Method – classification process
A seed set (Lp, Ln) is provided as input
Lexical relations (e.g. synonymy) from a thesaurus, or online dictionary, are used to extend seed set. Once added to the original ones, the new terms yield two new, richer sets Trp and Trn; together they form the training set for the learning phase of Step 4.
For each term ti in Trp∪Trn or in the test set, a textual representation of ti is generated by collating all the glosses of ti as found in a machine-readable dictionary. Each such representation is converted into vectorial form by standard text indexing techniques.
A binary text classifier is trained on the terms in Trp∪Trn and then applied to the terms in the test set.
Experiments
Classifier : NB, SVM, PrTFIDF
87.38% Accuracy
Term classification process is described in this figure.
A seed term set is provided as input.
And lexical relations from a thesaurus, are used to extend the seed set.
Once added to the original one, the new terms yield two new, richer sets Trp and Trn.
For each term ti in extended see set or Test set is expressed as a vectorial form.
A binary text classifier is trained by a seed set and applied to terms in the test set.
For Naïve bayesian, SVM, and PrTFIDF classifier,
eighty seven percent accuracy was obtained.
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13. Development of Linguistic Resource - Summary
Method
Intuition
Accuracy
Characteristics
Conjunction
Adjectives in and conjunctions usually have similar orientation, though but is used with opposite orientation
78.08%
The First try
test data : 1336 adjectives
PMI method
terms with similar orientation tend to co-occur in documents
87.13%
No limitation
Much time required
WordNet
Expansion
adjectives usually share the same orientation as their synonyms and opposite orientation as their antonyms
N/A
Limited to WordNet
Gloss Use
terms with similar orientation have similar glosses
terms without orientation have non-oriented glosses
87.38%
SentiWordNet (All word in WordNet)
Accuracy depends on the quality of thesaurus
This is a summary of development methods of linguistic resource.
As shown in this table, gloss use method demonstrated the best level of accuracy in classifying term orientation.
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14. Sentiment Classification
The process of identifying the sentiment – or polarity – of a piece of text or a document.
Document-level
Sentence-level, phrase-level
Feature-level
Define target of the opinion and assign the sentiment of the target
Document-level Sentiment Classification Method
PMI method
Machine Learning Method
Default Classifiers
Enhanced Classifier
NLP Combined Method
A Two-Step Classification
Combining Appraisal Theory
Sentiment classification is the process of identifying the sentiment of a piece of text or a document.
It can be classified into document-level, sentence-level, phrase-level, or feature-level classification.
For feature-level sentiment classification, the target of the opinion is defined and the sentiment of the target is assigned.
Now, I’ll introduce three document-level sentiment classification methods.
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15. PMI Method Turney et al., 2002 Process Experiments
Only two-word phrases containing adjectives or adverbs are extracted
Semantic orientation of a phrase
SO(phrase) = PMI(phrase, “excellent”) – PMI(phrase, “poor”)
Semantic orientation is an average semantic orientation of the phrases
Experiments
410 reviews from Epinions (epinion.com): 170 positive, 240 negative
calculating the PMI of 10,658 phrases from 410 reviews consume about 30 hours
Domain of review
Accuracy
Automobiles
84.00%
Movies
65.83%
- Honda Accord
83.78%
- The Matrix
66.67%
- Volkswagen Jetta
84.21%
- Pearl Harbor
65.00%
Banks
80.00%
Travel Destination
70.53%
- Bank of America
78.33%
- Cancun
64.41%
- Washington Mutual
81.67%
- Puerto Vallarta
80.56%
Turney et al. used PMI method to classify a document according to its polarity.
They used only two-word phrases containing adjectives or adverbs.
Semantic orientation of a phrase are determined by calculating PMI with the term excellent and poor.
And semantic orientation of a document is determined by an average semantic orientation of the phrases.
They experimented with four hundreds and ten reviews from epinion dot com.
The difference of the classification accuracy within the same domain isn’t large except travel domain.
And the difference between automobiles and movies is large.
It seems to be caused by the words used in each domain.
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16. ML - Default Classifier
Pang and Lee, 2002
A special case of text categorization with sentiment- rather than topic-based categories
Document modeling
standard bag-of-features framework
Experiments
Data : movie reviews (Internet Movie Database), rating -> negative, neutral, positive
Naïve Bayes, Maximum Entropy, Support Vector Machine
In terms of relative performance, Naïve Bayes tends to do the worst and SVM tends to do the best, although the differences aren’t very large.
Features
# of features
Frequency
or presence? NB ME
SVM
unigrams
16165
freq.
78.7
N/A
72.8
pres.
81.0
80.4
82.9
unigrams+bigrams
32330
80.6
80.8
82.7
bigrams
77.3
77.4
77.1
unigrams+POS
16695
81.5
81.9
adjectives
2633
77.0
77.7
75.1
top 2633 unigrams
80.3
81.4
unigrams+position
22430
80.1
81.6
Pand and Lee considered the document-level sentiment classification as a special case of text categorization with sentiment rather than topic-based categories.
They used standard bag-of-features framework, so that a document is expressed as a feature-frequency vector.
Three classifier was used.
Naïve bayes, maximum entropy and support vector machine.
They experimented with movie reviews from Internet Movie Database.
They selected only reviews where the author’s rating was expressed either with stars or numerical values.
Ratings were automatically extracted and converted into one of three categories: positive, negative, or neutral.
In terms of relative performance,
Naïve bayes tended to do the worst and SVM tended to do the best,
although the differences aren’t very large.
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17. ML - Using Only Subjective Sentences
Pang and Lee, 2004
improved polarity classification by removing objective sentences
A subjectivity detector determines whether each sentence is subjective or not
Standard subjectivity classifier
Subjectivity classifier using proximity relationship
The use of subjectivity extracts can improve the polarity classification at least no loss of accuracy.
After their experiments of default polarity classifiers,
Pang and Lee improved polarity classification by removing objective sentences.
A subjectivity detector determines whether each sentence is subjective or not,
Only subjective sentences are provided to the default classifier as input.
They implemented two subjectivity classifier.
One is the standard subjectivity classifier classifies each sentence in isolation.
And the other uses proximity relationship between sentences.
Their experiment showed that the use of subjectivity extracts can improve the polarity classification at least no loss of accuracy.
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18. NLP Combined Method – A Two-Step Classification
Wilson et al., 2005
A Two-Step Contextual Polarity Classification
employ machine learning and 28 linguistic features
document polarity : the average polarity of phrases
Step 1. Neutral-polar classifier classifies each phrase containing a clue as neutral or polar
Step 2. Polarity classifier takes all phrases marked in step 1 as polar and disambiguates their contextual polarity (positive, negative, both, or neutral).
28 Features : were extracted using NLP techniques with a dependency parser
4 Word Features, 8 Modification Features, 11 Structure Features, 3 Sentence Features, 1 Document Feature
Experiments
Data : Multi-perspective Question Answering (MPQA) Opinion Corpus
Wilson et al. combined NLP techniques in document-level sentiment classification.
They used a two-step contextual polarity classification employing machine learning techniques and 28 linguistic features.
Document polarity is the average polarity of phrases in the document.
At step one, neutral-polar classifier classifies each phrase containing a clue as neutral or polar.
At step two, polarity classifier takes all polar phrases and disambiguates their contextual polarity.
28 features extracted using NLP techniques with a dependency parser.
Wilson et al. experimented with MPQA Corpus.
At both neutral-polar classification and polarity classification,
linguistic features improved the classification performance.
neutral-polar classification (%)
polarity classification (%).
Features
Accuracy
Word token
73.6
Word+priorpol
74.2
28 features
75.9
Features
Accuracy
Word token
61.7
Word+priorpol
63.0
10 features
65.7
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19. NLP Combined Method - Combining Appraisal Theory
Whitelaw et al., 2005
applied the appraisal theory to the machine learning methods of Pang and Lee
Structure of an appraisal
An example “not very happy”
Experiments
a lexicon of 1329 appraisal entities have been produced semi-automatically from 400 seed terms in around twenty man-hours
combining attitude type and orientation : accuracy 90.2%.
Whitelaw et al. applied appraisal theory to the machine learning methods of Pang and Lee.
They defined the structure of an appraisal encompassing sentiment related properties like this figure.
The appraisal has four attributes: attitude, graduation, orientation, and polarity.
In an example “not very happy”, appraisal of each word is defined
and finally it is extended to the final phrase.
They constructed semi-automatically a lexicon of over thirteen hundreds appraisal entities.
When combining attitude type and orientation, they obtained the best accuracy about ninety percent.
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20. Sentiment Classification - Summary
Method
Characteristics
Pros
Cons
PMI Method
Use phrase PMI
Simple
Need not priory polarity dictionary
Loss of contextual meaning
Slow(Time to get PMI)
Machine Learning Method
Bag of Words
Unigram to bigram or n-gram
SVM, NB, MaxEnt
Need learning phase
NLP Combined Method
Based on ML
Parsing or Syntactic Analysis
Prior polarity to contextual polarity
Consider contextual meaning
Easily extendible for various purpose
Need prior polarity dictionary
Syntactic Analysis Overhead
I think NLP combined method is the best method for document-level sentiment classification that considers contextual polarity.
But it needs a dictionary containing terms whose prior polarity was determined.
And also it brings syntactic analysis overhead.
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21. Extracting and Summarizing Opinion Expression
Goal
Extract the opinion expression from large reviews and present it with an effective way
Tasks
Feature Extraction
Sentiment classification at the feature-level requires the extraction of features that are the target of opinion words
Sentiment Assignment
Each feature is usually classified as being either favorable or unfavorable.
Visualization
Extracted opinion expression are summarized and visualized.
Methods
Statistical Approaches
ReviewSeer (2003)
Opinion Observer (2004)
Red Opal (2007)
NLP-Based Approaches
Kanayama System (2004)
WebFountain (2005)
OPINE (2005)
product
Summarize
Extract
Features
I investigated six systems whose goal was to extract opinion expressions from large reviews on the Web and present it in an effective way.
Usually these systems perform three tasks.
Feature extraction, sentiment assignment, and visualization.
Sentiment classification at the feature-level requires the extraction of features that are the target of opinion words.
And each feature is usually classified as being either favorable or unfavorable.
For effective presentation, extracted opinion expression are summarized and visualized.
I divided the systems into two groups according to their underlying foundation.
And I’ll introduce two representative systems.
Assign
Sentiment
product
reviews
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22. Opinion Observer - Overview
Hu and Liu, 2005
Extract and summarize opinion expression from customer reviews on the Web.
Only mines the features of the product on which the customers have expressed their opinions and whether the opinion are positive or negative
Overall process
Review crawling
Feature extraction
Sentiment assignment
Opinion word extraction
Opinion orientation identification
Summary generation
Overall process
Opinion Observer extracts and summarizes opinion expression from customer reviews on the Web.
It only mines the features of the product on which the customers have expressed their opinions and whether the opinion are positive or negative.
This figure shows the overall process of the opinion observer.
At first, reviews are crawled from the Web.
Then, frequent feature are extracted.
Afterward, opinion words are extracted and orientation of the words are identified.
During this phase, infrequent features are identified.
At last, orientation of opinion sentences are identified and summary is generated.
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23. Opinion Observer - Tasks
Feature Extraction
Product features are extracted from the noun or noun phrase by the association miner CBA
Compactness pruning, redundancy pruning
Sentiment Assignment
Opinion sentence : a sentence contains one or more product features and one or more opinion words
Adjectives are the only opinion words
Prior polarity of adjectives was identified by WordNet expansion methods with seed terms
Infrequent features are extracted by using frequent opinion words
Polarity of a sentence is assigned as a dominant orientation
Extracted form : (product feature, # of positive sentences, # of negative sentences)
Experiments
Large collection of reviews of 15 electronic products
86.3% recall, 84.0% precision
Product features are extracted from the nouns or noun phrases by the association miner, CBA.
And compactness pruning and redundancy pruning are applied to remove unlikely feature.
In this system, opinion sentence is defined as a sentence that contains one or more product features and one or more opinion words.
Adjectives are the only opinion words and prior polarity of them was identified by WordNet expansion methods with seed terms.
And infrequent features are extracted by using frequent opinion words.
Polarity of a sentence is assigned as a dominant orientation.
At last, opinion expression is extracted as the following form.
Product feature, the number of positive sentences, and the number of negative sentences.
From their experiments with large collection of reviews of fifteen electronic products,
eighty six percent recall and eight four percent precision were obtained.
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24. Opinion Observer - Visualization
Features of products are compared by the bar graph
Number of positive and negative sentences of each feature are normalized
Positive portion
The number of positive and negative sentences of each feature are normalized and visualized using bar graph.
With this interface, customers can compare several products at the same time.
Negative portion
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25. Web Fountain - Overview
Yi et al., 2005
Extracts target features of the sentiment from the various resources and assigns polarity to the features
System Architecture
Sentiment Miner
Analyzes grammatical sentence structures and phrases by using NLP techniques
Web Fountain extracts target features of the sentiment from the various resources and assigns polarity to the features.
Sentiment Miner of the Web fountain analyzes grammatical sentence structures and phrases by using NLP techniques.
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26. Web Fountain – Tasks Feature Extraction Sentiment Assignment
Candidate features
a part-of relationship with the given topic
an attribute-of relationship with the given topic.
an attribute-of relationship with a known feature of the given topic
bBNP (Beginning definite Base Noun Phrase) heuristic is used
Select bnp (base noun phrase) that has high likelihood ratio
Experiments
Precision - digital camera: 97%, music reviews: 100%
Sentiment Assignment
Parse and traverse with two linguistic resources
Sentiment lexicon: define the sentiment polarity of terms
Sentiment pattern database: contain the sentiment assignment patterns of predicates
Product review
Recall 56%, Precision 87%
In the system, bBNP heuristic is used to extract candidate features.
Candidate features satisfy one of these relationships.
Base noun phrases that has high likelihood ratio are selected as features.
Their experiment with digital camera and music reviews showed the high precision.
In order to assign sentiment to the extracted features,
Reviews are parsed and traversed with two linguistic resources.
Sentiment lexicon defines the sentiment polarity of terms.
And sentiment pattern database contains the sentiment assignment patterns of predicates.
Experiment with product review,
fifty six percent recall and eight seven percent precision were obtained.
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27. Web Fountain – Visualization
Web interface listing sentiment bearing sentences about a given product
Web Fountain has a web interface listing sentiment bearing sentences about a given product.
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28. Extracting and Summarizing Opinion Expression - Summary
System
Feature Extraction
Sentiment Assignment
Visualization
Statistical
ReviewSeer
(2003)
N/A
probabilistic model
Naïve Bayes
Accuracy: 85.3%
List feature term and it’s score and show sentences contain the feature term
Opinion Observer
(2004)
CBA miner
Infrequent feature selection
WordNet expansion
prior polarity of adjectives
graph
Recall: 86.3%
Precision: 84.0%
Red Opal
(2007)
frequent noun and noun phrase
Precision:85%
use user’s rating
Precision:80%
ordered product list by score of each feature
the confidence of the scoring
NLP-based
Kanayama’s system
sentiment unit
modifying the machine translation framework
Recall:43%
Precision:89%
WebFountain
(2005)
bBNP heuristics
likelihood ratio
Precision:97%
sentiment lexicon
sentiment pattern database
Recall:56%
Precision:87%
listing sentiment bearing sentences of a product
OPINE
Web PMI
Recall:76%
Precision:79%
Relaxation Labeling
Recall:89%
Precision:86%
This is a summary of six systems that I investigated.
ReviewSeer extracts feature terms and have a web interface which lists feature term and it’s score and shows sentences contain the feature term.
Opinion Observer uses CBA miner to extract product feature,
And uses the prior polarity of adjective to assign sentiment to the features.
And it enables the comparison of products with the bar graph interface.
RedOpal is a recent system.
It uses user’s rating to assign score to extracted feature.
It lists products in a descending order based on the score of the selected feature.
Kanayama et al. defined the sentiment unit.
And modifying the machine translation framework, sentiment units were extracted.
High precision was obtained.
In WebFountain, bBNP heuristics was used to extract product feature.
And sentiment of each feature was assigned using NLP techniques with two linguistic resources.
OPINE uses Web PMI to extract product features.
And relaxation labeling was used to assign sentiment.
Although it is hard to compare the performance of these systems due to the lack of standard test data.
But in general,
statistical approaches showed higher recall than NLP based approaches.
And NLP-based approaches showed higher precision than statistical approaches.
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29. Discussion
OM is a growing research discipline related to various research areas, such as IR, computational linguistics, TC, TS, and DM.
Surveyed three topics and summarized it.
For Korean OM?
There isn’t any published research into the Korean OM.
Language differences may impose some limits on the methods used in the OM subtasks.
Structural differences between English and Korean may mean that the same heuristics cannot be applied to extract features from text
The lack of Korean thesaurus similar to WordNet limits the methods of obtaining the prior polarity of words for the PMI or conjunction methods.
Research into Korean OM must be conducted in conjunction with other related areas.
The final section of my presentation is a discussion.
Opinion mining is a growing research discipline related to various research areas, such as IR, computational linguistics, TC, TS, and DM.
I investigated three topics of opinion mining and summarized it.
There hasn’t been any published research using the Korean language yet.
Language differences may impose some limits on the methods used in the OM subtasks.
For example,
Structural differences between English and Korean may mean that the same heuristics cannot be applied to extract features from text.
The lack of Korean thesaurus similar to WordNet limits the methods of obtaining the prior polarity of words for the PMI or conjunction methods.
Because of these reason, I think that research into Korean OM must be conducted in conjunction with other related areas.
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30. Discussion - Research Map of OM
This research map shows the research flow of opinion mining.
I introduced four method for developing linguistic resources, and three method for sentiment classification.
I investigated six systems to extract and summarize opinion expression from reviews.
Statistical approaches shows higher recall and NLP-based approaches higher precision.
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31. Thank you My presentation is over. Thank you very much.
Have you any question?
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