Transcript 幻灯片 1

Sentiment Analysis Based
on Chinese Thinking Modes
Yang Liang
Outline
 Introduction
 Thinking models
 Description of Chinese Sentiment Expression
Model
 Implicit Chinese Sentiment Expression Mining
Based on LSA
 Experiment Setting and Evaluation
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Introduction
 Sentiment analysis ——a popular research topic
in NLP in recent years
◆ Blog ,twitter, comment
 Sentiment analysis in China
◆ phase level, sentence level(COAE)
◆ Less work in passage level
 Our work
◆ relationships between thinking modes and language
◆ Chinese sentiment expression model and LSA
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Outline
 Introduction
 Thinking models
 Description of Chinese Sentiment Expression
Model
 Implicit Chinese Sentiment Expression Mining
Based on LSA
 Experiment Setting and Evaluation
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Thinking models
 Spiral Graphic Mode and Straight Line Mode
 Concreteness and Abstractness
 Scatter view and Focus view
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Spiral Graphic Mode and
Straight Line Mode
 Spiral Graphic Mode
◆ reflects in the passage organization
◆ the topic of the passage is discussed after examples
 Straight Line Mode
◆ focus on deduction and thinking in a straight line way
◆ tend to state their views directly and frankly
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Spiral Graphic Mode and
Straight Line Mode
 Example
◆ (1) Chinese:
“他被眼前的一幕震惊了。”
English:
“He was shocked by what he saw.”
◆ (2) Chinese:
“经过反复的思考，我终于得到了完美的答案。”
English:
“I got a perfect answer after deeply thinking.”
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Thinking models
 Spiral Graphic Mode and Straight Line Mode
 Concreteness and Abstractness
 Scatter view and Focus view
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Concreteness and
Abstractness
 Concreteness
◆ Chinese uses quantities of specific words, shapes,
sounds and description to illustrate abstract things
 Abstractness
◆ English tend to implement general vocabularies and
their variants to express abstract feelings or opinions,
such as “-ion”, “-ance” and “-ness”
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Concreteness and
Abstractness
 Example
◆ (1) Chinese:
土崩瓦解。
English:
Disintegration
◆ (2) Chinese:
有志者，事竟成
English:
When there is a will , there is a way.
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Thinking models
 Spiral Graphic Mode and Straight Line Mode
 Concreteness and Abstractness
 Scatter view and Focus view
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Scatter View and
Focus View
 Scatter View
◆ Chinese tend to emphasize unified whole
◆ Example : more than one verb is used in one Chinese
sentence
 Focus View
◆ English pay more attention to logical reasoning or
deduction
◆ express their feelings or emotions briefly thinking
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Scatter View and
Focus View
 Example
◆ (1) Chinese:
他拿着课本走进了教室。
English:
He walked into the classroom with a textbook in hands.
◆ (2) Chinese:
他们俩青梅竹马，两小无猜
English:
The boy and the girl were playmates in their childhood.
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Outline
 Introduction
 Thinking models
 Description of Chinese Sentiment Expression
Model
 Implicit Chinese Sentiment Expression Mining
Based on LSA
 Experiment Setting and Evaluation
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Description of
CSE Model
 Quantification of “Spiral Graphic Mode”
◆ emotion-determining words mostly locate the end part
of Chinese sentences
◆ the closer ai locates the tail, the larger the score(ai)
will be
score  A   1  position  ai  count  a | A   score  ai 
i
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Description of
CSE Model
 Quantification of “Concreteness”
◆ Chinese sentiment expression, verb also plays an
important role，“脸红”“溃败”
◆ the highest priority to adjective, then the higher
priority is given to the verb, finally other words are
processed
n
score  a   Wi
i
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m
adj
 W
j
l
verb
j
 Wk
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Description of
CSE Model
 Quantification of “Scatter View”
◆ View window to simulate the Scatter View
◆ fixed at 6
 Extend Resource
◆ CRF，COAE
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Description of
CSE Model
 Quantification of Similarities between Thinking
Modes
◆ The similarities between Chinese and English
◆ Example:
1) Chinese: “这个酒店什么都好，就是服务让人失望。”
English: “Every aspect about the hotel is ok except the
disappointing service.”
2) 脸红，blush
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Outline
 Introduction
 Thinking models
 Description of Chinese Sentiment Expression
Model
 Implicit Chinese Sentiment Expression Mining
Based on LSA
 Experiment Setting and Evaluation
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Implicit Chinese Sentiment
Expression Mining Based on LSA
 The Criterion of Implicit Emotion Sample
◆ Emotion expressed in an indirect way
◆ the implicit emotion articles ,low scores
◆ group of samples are chosen to determine the
threshold,(from DUTIR Emotion Ontology )
 Implicit Emotion Classification Based on LSA
◆ LSA, relationships between the implicit samples and
the seed samples
score j  Ai    sim  Ai , s jk   score  s jk 
k
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Outline
 Introduction
 Thinking models
 Description of Chinese Sentiment Expression
Model
 Implicit Chinese Sentiment Expression Mining
Based on LSA
 Experiment Setting and Evaluation
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Experiment Setting
and Evaluation
 Experiments of Chinese Thinking modes in
different domains
◆ Corpus , three domains, 4000 hotel reviews, 1608
electronics reviews and 1047 stock reviews
◆ result
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Experiment Setting
and Evaluation
 Elec
◆ adding each Chinese thinking mode the precision has
increased.
◆ elec-pos reviews is not increasing obviously——too
much noun
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Experiment Setting
and Evaluation
 Stock
◆ stock-neg data set is not good
◆ great number of specialized words exist and part of
them does not appear in DUTIR emotion ontology
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Experiment Setting
and Evaluation
 Experiment of Chinese sentiment expression
model and LSA
◆ Experiment results on ChnSentiCorp
Lexicon
Semantic
CSE
Pos
1575/1828(86.16%)
1502/1828(82.17%)
1575/1828(86.16%)
Neg
1727/2163(79.84%)
1811/2163(83.73%)
1827/2163(84.47%)
Total
3302/3991(82.74%)
3313/3991(83.01%)
3402/3991(85.24%)
◆ Statistic data of implicit samples
Pos-Implicit
Neg-Implicit
Total
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Lexicon
751/1575(47.68%)
836/1827(45.76%)
1587/3402(46.65%)
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CSE
640/1575(40.63%)
624/1827(34.15%)
1264/3402(37.15%)
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Experiment Setting
and Evaluation
 Secondary classification by implementing LSA
◆ Macro-Average-Precision of different methods in
ChnSen
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Thanks for your attention
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