Affective Lexicon in Chinese中文情緒詞庫建構與標記

Pei-Yu, Lu

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Outline

Introduction

Background Knowledge

Methodology

Results

Introduction

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Motivation

The Demand for Information on Opinions and Sentiment Consumer Reports

Applications Review-Related Websites Business and Government

Intelligence

Fundamental of Sentiment Analysis Affect Lexical Resources

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Comparison with Existing Affect Lexicon Resources

Resource WordNet-Affect

HowNet-Affect

NTUSD C-LIWC Affect

CHEN,2009

Lu,2015

Amount 4,787 8,936 11,086 3,016 17,156 ?

Polarity No Yes Yes Yes/No No Yes

Emotion Category

No No No Yes/No Yes Yes

Labels Yes No No No No Yes

Insulting Words

No No No Yes No Yes

Beyond Words (chunks)

No No No/yes No No Yes

Focus Synsets Evaluation, stance, strength

Binary polarity

High frequency

Emotion category

Emotion-signaling

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Affective Lexicon

in Chinese

Affect-Denoting

Emotion Mood

Temperament/

Personality

Affect-Signaling

Expletives

Emotive Prosody

Interjections

Collocates

Emoticons

Words/phrases

Chunks

Language Units

Structure of the Lexicon

Background Knowledge:Basic emotions

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Theorist Basic Emotions

Plutchik Acceptance, anger, anticipation, disgust, joy, fear, sadness, surprise

Arnold Anger, aversion, courage, dejection, desire, despair, fear, hate, hope, love, sadness

Ekman, Friesen, and Ellsworth

Anger, disgust, fear, joy, sadness, surprise

Frijda Desire, happiness, interest, surprise, wonder, sorrow

Gray Rage and terror, anxiety, joy

Izard Anger, contempt, disgust, distress, fear, guilt, interest, joy, shame, surprise

James Fear, grief, love, rage

McDougall Anger, disgust, elation, fear, subjection, tender-emotion, wonder

Mowrer Pain, pleasure

Oatley and Johnson-Laird

Anger, disgust, anxiety, happiness, sadness

Panksepp Expectancy, fear, rage, panic

Tomkins Anger, interest, contempt, disgust, distress, fear, joy, shame, surprise

Watson Fear, love, rage

Weiner and Graham Happiness, sadness

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Starting from the Ekman group of anger, fear, surprise, disgust, happiness and sadness, Jack et al. (2014) analyzed the 42 facial muscles which shape emotions in the face and came up with only four basic emotions found fear and surprise are similar, with 'eyes wide open' as the person increases visual attention. Anger and disgust are also similar, both starting with nose wrinkling.

Four Basic Emotions

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Table 1. 50 Finnish emotion concepts used in the experiment, and their English translations.

Networks of Emotion Concepts:

1. Rank most frequent 50 emotion concepts

2. Evaluate the similarity of them in pairs via cognitive experiment

3. By multidimensional scaling method, output the clusters

(Toivonen, 2012)

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Cluster hierarchy of the Average Similarity Network.

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The combination of 4 basic emotions with the clusters

Emotion

Negative

Anger

Angered

Disgusted

Doubting

Sadness

Despairing Depressed Missing

Fear

Afraid

Positive

Happy

Calm Cheerful Love

Surprise

Background Knowledge:Emotion, Mood, and Temperament

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Affect theory

Affect is a more general psychological construct that refers to mental states about what is happening to them (Parkinson et al., 1996)

The conceptual schemes, such as basic emotions and dimensions, are models of affect that can be applied to both moods and emotions. (Parkinson et al., 1996)

However, moods and emotions are characterized by several important differences: comprehensiveness, duration, frequency/intensity, and pattern of activation. (Davidson, 1994; Ekman, 1994; Watson & Clark, 1994a)

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Emotion Mood Temperament

Duration Brief, short term,Lasting seconds

Long term, pervasive, changing state of mind, lasting minutes to days

Lifelong, trait-like; stable over periods of months to years

Object Focused on a particular object or event; response system

Unfocused Applied to pertinent situations or events

Intensity High intensity/ activation

Low to moderate intensity/activation

--

Frequency Infrequent occurrence

Frequent, continuous, changing occurrence

Stable and organized throughout development

Function Adaptive, to focus attention, provide information to the organism

To instigate, facilitate, sustain, and modify active engagement with the environment

Influences emotional reaction, cognition, and behavior

Type Entity Brief state Longer term state Trait or disposition, individual difference variable

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Emotion vs. Mood

Compared with emotion, mood is much broader and more inclusive Milder version of classic emotions Eg. Cheerful(joy), tense(fear) Complex combinations of more basic affects E.g fatigue, relaxation

Moods have a much longer duration than emotions (Davidson, 1994, Ekman, 1994; Watson & Clark, 1994a) Emotion lasts for ½ to 4 or 5 seconds (Izard, 1991, pp.80-81)

E.g The full emotion of fear lasts for seconds to a minute

Mood lasts for hours or days Eg. Feelings of nervousness can last for hours (as one anticipates giving

public speech)

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Mood vs. Temperament

Mood Temperament

Similarity Subjectively experienced feelings

Differences:

1. The state/trait dimension Current state of mind Basic characteristics of a trait

2. Duration Hours or days Years to decades

3. Organization/stability Evanescent and ever-changing

Reliable and recurring

Temperament subsequently influences emotion thinking, and behavior in an organized and consistent way. (Goldsmith, 1994; Watson & Clark, 1994b; Watson & Walker, 1996)

Mood reflect more flexible systems that are constantly being influenced by the internal and external factors.

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Temperament vs. Personality

Temperament Personality

Similarity Stable, trait-like characteristics

Difference

Elementary and fundamental traits that influence the subsequent development of broader individual differences in personality

Not necessarily biologically based or heritable; may reflect biological influences, environmental factors, or some combination of the two.

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Temperament and PersonalityEssentially dimensions of temperament: Extroversion and neuroticism (Clark & Watson, 1999; Watson & Clark, 1992)Primarily temperamental dimension: agreeableness and conscientiousness (Clark & Watson, 1999; Costa & McCrae, 1992; Strelau, 1998)

Openness represent a temperament?

The general traits of personality show strong and consistent relation with temperamental constructs.

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Affective Lexicon in Chinese

Affect-Denoting

Emotion

1.Happiness2. Sadness

3. Fear4. Anger

5. Surprise

Mood

1. Cheerful2.Distressed3. Nervous4. Irritable

Temperament/

Personality

1. Well-being2. Depression

3. Anxiety4. Hostility

Affect-Signaling

Expletives

Emotive

Prosody

Interjection

s

Collocates

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Affective Lexicon

in Chinese

Affect-Denoting

Emotion Mood

Temperament/

Personality

Affect-Signaling

Expletives

Emotive Prosody

Interjections

Collocates

Emoticons

Words/phrases

Chunks

Language Units

Structure of the Lexicon

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Semantic Prosody (SP)

Three defining features of SP: 1. Functionality: the initial meaning choice will actually be

at the functional level of the SP (Sinclair, 1996: 87) 2. Linguistic choice: The combination of every collocation

is not in the least arbitrary. But all words are in a mutually selectional relation.

3. Communicative purpose: SP are “attitudinal and on the pragmatic side of the semantics-pragmatics continuum” (Sinclair, 1996: 87).

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Definition of SP

“The habitual co-occurrence of two or more words” (Stubbs, 1996: 176)

“The spreading of connotational coloring beyond single word boundaries” (Partington,1998: 68)

Stubbs (1996) classifies SP into three categorizations: negative prosody, positive prosody and neutral prosody.

E.g. Negative prosody: 遭到 批評 / ＊遭到 讚美

Methodology

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Steps

How to construct an affective units database?

Step1. Collect the data

Step2. Annotate the data

Step3. Apply / Evaluate the data

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Step 1. Collect the Affective Units

Affective Units

Emotion-denoting

Emotion-signaling

Expletives

Emoticons

Interjections

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1.1 Collecting Emotion Words

Emotion Words

卓淑玲情緒詞 218

C-LIWC

1492

CILIN selection

捷進寫作詞彙

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1.1 Definition of Emotion Describing Words

Emotion Describing Words:

1. 描述主觀情緒經驗

2. 動作表情

3. 認知狀態

4. 生理反應

5. 因應方式

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1.2 Collecting Expletives

Dirty words

physical peculiarities and appearance

social relationships

real or imagined mental traitsbody processess and

products

Sex insult

animal names

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1.3 Emoticons

Emoticons

Surprise

NegativePositive

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1.4 Chunks from Web Annotation

The source of the texts

PTT Boards

happy Sad marvel Hate

Yahoo News

happy depressing

worried odd angry

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Rules for Annotating Chunks

Aspect: Author’s attitude, not reader’s

Emotion Categories: categorize the words/phrases/chunks into 5 emotions. (take the reference A of next page) If the unit could not be categorized into the 5 emotions, put them

into others(positive or negative)

Howto: Highlight the unit that signaling emotion.

Demo http://lopen.linguistics.ntu.edu.tw:7777/emilytagger/

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Reference A (detailed categories of 5 emotions)

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Step 2. Annotation of the Data

Emotion Units

Emotion Category

Polarity Mood

Temperament/

Personality

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Affect During Time

PersonalityMoodEmotion

Results

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The Amount of Annotation (Cont.)

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The Amount of Annotation

Total amount: 5964

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PTT-happy

Happy

40

PTT-Sad

Sad

41

PTT-Marvel

Scary/ Nervous

42

PTT-Hate

Angry

43

Yahoo-Depressing

Sad

44

Yahoo-Angry

Angry

45

Yahoo-Worried

Scary/ Nervous

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Yahoo-Odd

Surprising

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Yahoo-Happy

Happy

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Emoticons

Happy; 142

Sad; 55

Nervous; 13

Angry; 30

Surprise; 24 Other; 3

Total: 267

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Interjections

Happy; 19

Sad; 7

Nervous; 1Angry; 14

Surprise; 14

Other; 12

Total: 67

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Expletives

Expletives; 72

Animal-related abusive; 21

Intellectual/mental abusive; 29

Sex-related abusive; 27

Social-re-lated

abusive; 122

Appearance-related abusive; 40

Total: 335

The Prediction-Ability of the Chunks

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The Method of Counting Predictive Valence

1. Calculate the emotion polarity of 10 words after each keyword pos=1, neg=-1,neu=0

x+y+z=10emo_polarity=pos*x+neu*y+neg*z

2. Calculate the average valence of each keyword X = The frequency of Keyword following by positive polarity

Y = The frequency of Keyword following by negative polarityZ = The frequency of Keyword following by neutral polarityX+Y+Z= Total Frequency of the KeywordPredictive_Valence = [X*1+y*(-1)]/X+Y+Z

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The comparison with high-frequency chunks

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To Evaluate the Predictive Valence

List the top-30-frequency chunk of each basic emotion class 7 categories, total 128

chunks (delete the emotion-denoting words)

Consistency of emotion polarities (positive/negative) between tags and valence: 55%

Lemma Emo_VAL

Emo_Class

Consis-tency

疑似 0.40816 生氣 ,

其他（負） ,

驚訝

Complex

!!! 0 高興 No

仍 0.02419 其他（負）

No

^^ 0.33654 高興 Yes

吸毒 -0.97059 負 Yes

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Chunk Sample Experiment

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The Method of Chunk Sample Experiment

Random 20 samples of chunks according to its predictive valence

Extract 10 example sentences of each chunk from PTT Corpus and manually annotate the polarity of each sentence

Compare the accuracy of pure a-bag-of-words method to chunk-signaling method

Chunk Predict_Val

Chunk Predict_Val

灑花 1 幹你娘 -1

加油 0.92308 斥責 -0.91667

幸好 0.84211 何必 -0.8

還好 0.70833 只好 -0.73077

在一起 0.64 不然 -0.64865

雖然如此 0.5 你這種人 -0.5

總算 0.46154 不要這樣 -0.4

散步 0.36364 遭到 -0.34783

好不好 0.25 憑什麼 -0.22222

至少 0.10256 當初 -0.07895

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Example: 當初

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The Results of Chunk Sample Experiment

Chunk_Positive 提昇率 Chunk_Negative 提昇率

灑花 11.11% 幹你娘 25.00%

加油 0% 斥責 0%

幸好 16.67% 何必 28.57%

還好 0% 只好 11.11%

在一起 0% 不然 0%

雖然如此 0% 你這種人 14.29%

總算 0% 不要這樣 11.11%

散步 0% 遭到 0%

好不好 20.00% 憑什麼 16.67%

至少 0% 當初 75.00%

Q&A