sentiment analysis based on chinese thinking modes yang liang

Sentiment Analysis Based

on Chinese Thinking Modes

Yang LiangYang Liang

2 23/4/18ir.dlut.edu.cnir.dlut.edu.cn

Introduction

Thinking models

Description of Chinese Sentiment Expression Model

Implicit Chinese Sentiment Expression Mining Based on LSA

Experiment Setting and Evaluation

Outline


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

Introduction


Introduction

Thinking models




Outline


Spiral Graphic Mode and Straight Line Mode

Concreteness and Abstractness

Scatter view and Focus view

Thinking models


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

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.”

Spiral Graphic Mode and

Straight Line Mode





Thinking models


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”



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.






Thinking models


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

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.

Scatter View and Focus View


Introduction

Thinking models




Outline


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

Description of CSE Model

1 |i ii

score A position a count a A score a


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 m l

adj verbi j k

i j k

score a W W W


Quantification of “Scatter View”

◆ View window to simulate the Scatter View

◆ fixed at 6

Extend Resource

◆ CRF， COAE



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



Introduction

Thinking models




Outline


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

Implicit Chinese Sentiment Expression Mining Based on

LSA

,j i i jk jkk

score A sim A s score s


Introduction

Thinking models




Outline


Experiments of Chinese Thinking modes in different domains

◆ Corpus , three domains, 4000 hotel reviews, 1608 electronics reviews and 1047 stock reviews

◆ result



Elec

◆ adding each Chinese thinking mode the precision has increased.

◆ elec-pos reviews is not increasing obviously——too much noun



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



Experiment of Chinese sentiment expression model and LSA

◆ Experiment results on ChnSentiCorp

◆ Statistic data of implicit samples


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%)

Lexicon CSEPos-Implicit 751/1575(47.68%) 640/1575(40.63%)Neg-Implicit 836/1827(45.76%) 624/1827(34.15%)

Total 1587/3402(46.65%)

1264/3402(37.15%)


Secondary classification by implementing LSA

◆ Macro-Average-Precision of different methods in ChnSen



Thanks for your attention

sentiment analysis based on chinese thinking modes yang liang

Documents

scatter view chinese

concreteness chinese

focus view slide

straight line mode slide

abstractness slide

focus view thinking

abstractness scatter

lsa introduction slide