detecting emotive sentences with pattern...

DETECTING EMOTIVE SENTENCES WITH PATTERN-BASED

LANGUAGE MODELLING

Fumito Masui

Rafal Rzepka

Kenji Araki

Kitami Institute of Technology

Hokkaido University

PRESENTATION OUTLINE

1. Problem definition (emotive vs. non-emotive)

2. Language Modeling

3. Pattern Based Language Modeling

4. Experiment setup

5. Results and discussion

6. Conclusions and Future work

PROBLEM DEFINITION

MAINSTREAM:

POSITIVE VS. NEGATIVE

EMOTION TYPES

DISREGARDED OR AS SUBTASK:

IS THE SENTENCE EMOTIONAL / NEUTRAL (PRESELECTION)

PROBLEM DEFINITION

“Was the speaker in an emotional state?”

emotional neutral

PROBLEM DEFINITION


emotional neutral

Easy to ask laypeople

because everyone thinks

they are specialists in their

own emotions.

PROBLEM DEFINITION


emotional neutral

Easy to ask laypeople

because everyone thinks

they are specialists in their

own emotions.

1. Junko Minato, David B. Bracewell, Fuji Ren and Shingo Kuroiwa. 2006. Statistical Analysis

of a Japanese Emotion Corpus for Natural Language Processing. LNCS 4114, pp. 924-929

2. Saima Aman and Stan Szpakowicz. 2007. Identifying expressions of emotion in text. In

Proceedings of the 10th International Conference on Text, Speech, and Dialogue (TSD-

2007), Lecture Notes in Computer Science (LNCS), Springer-Verlag.

3. Alena Neviarouskaya, Helmut Prendinger and Mitsuru Ishizuka. 2011. Affect analysis

model: novel rule-based approach to affect sensing from text. Natural Language

Engineering, Vol. 17, No. 1 (2011), pp. 95-135.

PROBLEM DEFINITION

emotional neutral

subjective

objective


“Did the speaker present their the contents from first-person centric perspective or no specified perspective?”

PROBLEM DEFINITION

emotional neutral

subjective

objective



Doesn’t have much to do with emotions

Only expressions of emotions tend

to be first person centric as well.

PROBLEM DEFINITION

emotional neutral

subjective

objective






Opinion could be subjective but neutral

• “I think it will rain tomorrow.”

• “In my opinion the government should

have applied a different policy.”

** Not talking about positive/negative.**

PROBLEM DEFINITION

emotional neutral

subjective

objective






Opinion could be subjective but neutral

• “I think it will rain tomorrow.”

• “In my opinion the government should

have applied a different policy.”

** Not talking about positive/negative.**

1. Janyce M. Wiebe, Rebecca F. Bruce and Thomas P. O’Hara. 1999. Development

and use of a gold-standard data set for subjectivity classifications. In Proceedings

of the Association for Computational Linguistics (ACL-1999), pp. 246-253.

2. Theresa Wilson and Janyce Wiebe. 2005. Annotating Attributions and Private

States. Proceedings of the ACL Workshop on Frontiers in Corpus Annotation II, pp.

53-60.

3. Hong Yu and Vasileios Hatzivassiloglou. 2003. Towards answering opinion

questions: separating facts from opinions and identifying the polarity of opinion

sentences. In Proceedings of Conference on Empirical Methods in Natural Language

Processing (EMNLP-2003), pp. 129-136.

4. Vasileios Hatzivassiloglou and Janice Wiebe. 2000. Effects of adjective orientation

and gradability on sentence subjectivity. In Proceedings of International

Conference on Computational Linguistics (COLING-2000), pp. 299-305.

PROBLEM DEFINITION



emotional neutral

subjective

objective

subjective

PROBLEM DEFINITION



“Was the sentence expressed with emphasis (distinguishable linguistic emotive features)?”

emotivenon-emotive

emotional neutral

subjective

objective

subjective

PROBLEM DEFINITION

• All emotive sentences are emotional.

• All neutral and objective sentences are non-emotive.

• Some emotional sentences could be non-emotive.

• Subjective sentences could be emotive/non-emotive as well as emotional/neutral.

emotivenon-emotive

emotional neutral

subjective

objective

subjective

PROBLEM DEFINITION

Emotive/non-emotive could help distinguishing between

emotional/neutral

subjective/objective

emotivenon-emotive

emotional neutral

subjective

objective

subjective

PROBLEM DEFINITION

In linguistics:

Karl Buhler in 1934: 3 functions of language: descriptive, impressive, emotive

Stevenson in 1937: emotiveness (with regards to morality as a concept influenced by emotions)

Roman Jakobson in 1960: 6 functions of language: +poetic, +phatic, +metalingual

• Karl Buhler. 1990. Theory of Language. Representational Function of Language. John Benjamins Publ. (reprint from

Karl Buhler. 1934. Sprachtheorie. Die Darstellungsfunktion der Sprache, Ullstein, Frankfurt a. Main, Berlin, Wien,)

• Stevenson, C. L. 1937. The Emotive Meaning of Ethical Terms. In Stevenson, C. L. Facts and Values. Yale University

Press (published 1963). ISBN 0-8371-8212-3.

• Roman Jakobson. 1960. Closing Statement: Linguistics and Poetics. Style in Language, pp.350-377, The MIT Press.

PROBLEM DEFINITION

In linguistics:

Emotive elements: interjections/exclamations (aah, ooh, whoa, great!), hypocoristics (endearments, dog doggy), emotive punctuation (!, ??, …, ～),

emoticons (:-), ^o^), onomatopoeia/mimetic expressions (gitaigo in Japanese)

PROBLEM DEFINITION

In linguistics:



Plus - combinations of elements

ああ、今日はなんて気持ちいい日なんだ！(^O^)/Oh, what a pleasant day today, isn’t it ?! (^O^)/

InterjectionExclamative

phrase

Exclamative

grammar

Exclamation

markEmoticon

PROBLEM DEFINITION

In linguistics:





Are there non-emotive elements ??


phrase

Exclamative

grammar

Exclamation

markEmoticon

PROBLEM DEFINITION

In linguistics:





Are there non-emotive elements ??

How to extract them both?


phrase

Exclamative

grammar

Exclamation

markEmoticon

PROBLEM DEFINITION

ああ、今日はなんて気持ちいい日なんだ！(Oh, what a pleasant day today, isn’t it?)

This sentence contains the pattern:

ああ＊なんて＊なんだ！ (Oh, what a * isn’t it?)

1. This pattern cannot be discovered with n-gram approach.

2. This pattern cannot be discovered if one doesn’t know what to look for.

Need to find a way to extract such frequent sophisticated patterns

from corpora.

*) pattern = something that frequently appears in a corpus (more than once).

LANGUAGE MODELING

INTRODUCTION

Language modelling

HOW YOU IMAGINE IT

LANGUAGE MODELING

IN REALITY…

LANGUAGE MODELING

Statistical representation of a piece of language data

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram

Unordered set of words

The dog bit the man = The man bit the dog

- No grammar

- No word order

- Just a bag of words…

Harris, Zellig. 1954. Distributional Structure. Word, 10 (2/3), pp. 146-162.

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram



- No grammar

- No word order


Harris, Zellig. 1954. Distributional Structure. Word, 10 (2/3), pp. 146-162.

POPULAR IN

MACHINE

LEARNING

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram



- No grammar

- No word order


• Harris, Zellig. 1954. Distributional Structure. Word, 10 (2/3), pp. 146-162.

• E. Cambria and A. Hussain. 2012. Sentic Computing: Techniques, Tools, and Applications. Dordrecht, Netherlands: Springer.

• Yuanhua Lv and ChengXiang Zhai. 2009. Positional Language Models for Information Retrieval. In Proceedings of the 32nd

international ACM SIGIR conference on Research and development in information retrieval (SIGIR), pp. 299-306.

POPULAR IN

MACHINE

LEARNINGModifications:

• Positional Language Model

• Bag-of-concept

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram

Sentence = set of n-long ordered sub-sequences

of words.

The dog bit the man

2grams:

the dog | dog bit | bit the | the man

3grams:

the dog bit | dog bit the | bit the man

4grams:

the dog bit the | dog bit the man

• C. E. Shannon. 1948. A Mathematical Theory of Communication, The Bell System Technical Journal, Vol. 27, pp. 379-423 (623-656), 1948.

• A.A. Markov. Extension of the limit theorems of probability theory to a sum of variables connected in a chain. Reprinted in Appendix B of: R.

Howard. 1971. Dynamic Probabilistic Systems, Vol. 1: Markov Chains. John Wiley and Sons.

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


of words.

(1) John went to school today.

John went 👍went to 👍

John * school




👍

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram

• Xuedong Huang, Fileno Alleva, Hsiao-wuen Hon, Mei-yuh Hwang, Ronald Rosenfeld. 1992. The SPHINX-II Speech Recognition System: An Overview, Computer, Speech and Language, Vol. 7, pp. 137–148.

• Guthrie, D., Allison, B., Liu,W., Guthrie, L., &Wilks, Y. (2006). A closer look at skip-gram modelling. In Proceedings of the 5th international Conference on Language Resources and Evaluation (LREC-2006), pp. 1-4.

• Rene Pickhardt, Thomas Gottron, Martin Korner, Paul Georg Wagner, Till Speicher, Steffen Staab. 2014. A Generalized Language Model as the Combination of Skipped n-grams and Modified Kneser Ney Smoothing.

In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL 2014), pp. 1145-1154.

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram

Sentence = some words within an n-gram can be

skipped over



John * to 👍

3gram: John went to

1skip2gram: John _ to





LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


skipped over



John * to 👍

3gram: John went to

1skip2gram: John _ to

To do this

you need to…





LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


skipped over



John * school 👍

4gram: John went to school

2skip2gram: John _ _ school

Skip-gram model with

modified Kneser-Ney

Smoothing

…





LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


skipped over


(2) John went to this awful place many people

tend to generously call school today.


John * to 👍

John * school

John * to * today

👍

👍

And still don’t get

the whole picture.





LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


skipped over





John * to 👍

John * school

John * to * today

👍

👍

Skip-grams cannot

help extracting such

patterns because…





LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


skipped over





John * to 👍

John * school

John * to * today

👍

👍





1. The “skip” can

appear only in one

place.

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


skipped over





John * to 👍

John * school

John * to * today

👍

👍






appear only in one

place.

2. The same number of

skips needs to be

retained for each gap.

(Full control of the

skip-length.)

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


skipped over





John * to 👍

John * school

John * to * today

👍

👍






appear only in one

place.


skips needs to be



skip-length.)

w s{1} w s{1} w

w s{1} w s{10} w

≠

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram


skipped over





John * to 👍

John * school

John * to * today

👍

👍





w s{1} w s{1} w

w s{1} w s{10} w

≠NOT SO

POPULAR


appear only in one

place.


skips needs to be



skip-length.)

LANGUAGE MODELING

1. Bag-of-words

2. N-gram

3. Skip-gram

Solution &

simplification:

PATTERN BASED LANGUAGE MODELING

SPEC – Sentence Pattern Extraction arChitecture

Sentence pattern = ordered non-repeated combinations of sentence elements.

For 1 ≤ k ≤ n , there is all possible k-long patterns, and

• Michal Ptaszynski, Rafal Rzepka, Kenji Araki and Yoshio Momouchi. 2011. Language combinatorics: A sentence pattern extraction architecture based on combinatorial explosion.

International Journal of Computational Linguistics (IJCL), Vol. 2, Issue 1, pp. 24-36.





Extract patterns from

all sentences and

calculate occurrence.







Normalized pattern weight

Score for one sentence



Next,

classify/

compare

emotive

sentences

with non-

emotive





Normalized pattern weight

Score for one sentence




1. Bag-of-words

2. N-gram

3. Skip-gram

4. PBLM(all above + more)


skipped over





John * to 👍

John * school 👍

John * to * today 👍

EXPERIMENT SETUP

DATASET

91 sentences close in meaning, but different emotional load

(50 emotive, 41 non-emotive) gathered in an anonymous survey on 30

people of different background (students, businessmen, housewives).

Emotive

高すぎるからねTakasugiru kara ne

'Cause its just too expensive

すごくきれいな海だなあ

Sugoku kirei na umi da naa

Oh, what a beautiful sea!

なんとあの人、結婚するらしいよ

Nanto ano hito, kekkon suru rashii yo

Have you heard? She’s getting married!

Non-emotive

高額なためです。

Kougaku na tame desu.

Due to high cost.

きれいな海です

Kirei na umi desu

This is a beautiful sea

あの日と結婚するらしいです

Ano hito kekkon suru rashii desu

They say she is gatting married.

Examples:

EXPERIMENT SETUP

Preprocessing

Sentence: 今日はなんて気持ちいい日なんだ！

Transliteration: Kyōwanantekimochiiihinanda!

Translation: What a pleasant day it is today!

Preprocessing examples

1. Tokens: Kyō wa nante kimochi ii hi nanda !

2. POS: N TOP ADV N ADJ N COP EXCL

3. Tokens+POS:Kyō[N] wa[TOP] nante[ADV] kimochi[N] ii[ADJ] hi[N]

nanda[COP] ![EXCL]

EXPERIMENT SETUP

Pattern List Modification

1. All patterns

2. Zero-patterns deleted

3. Ambiguous patterns deleted

Weight Calculation Modifications

1. Normalized

2. Award length

3. Award length and occurrence

EXPERIMENT SETUP


1. All patterns



All patterns vs. only n-grams


1. Normalized

2. Award length


EXPERIMENT SETUP


1. All patterns





1. Normalized

2. Award length


Automatic threshold setting

EXPERIMENT SETUP


1. All patterns



10-fold Cross Validation



1. Normalized

2. Award length


Automatic threshold setting

One experiment

= 280 runs

EXPERIMENT SETUP

Score calculated in:

- Precision

- Recall

- Balanced F-score

- Accuracy

- Specificity

- Phi-coefficient

RESULTS AND DISCUSSIONTokenized Tokens + POS

Weig

ht

norm

aliz

ed

Length

aw

ard

ed


Weig

ht

norm

aliz

ed

Length

aw

ard

ed

specific elements

are more effective


Weig

ht

norm

aliz

ed

Length

aw

ard

ed

specific elements

are more effectivepatterns better

than n-grams


Weig

ht

norm

aliz

ed

Length

aw

ard

ed

specific elements


than n-grams

awarding

length yields

higher results

awarding

length yields

higher results


Weig

ht

norm

aliz

ed

Length

aw

ard

ed

specific elements


than n-grams

awarding

length yields

higher results

awarding

length yields

higher results

F=0.76

P=0.64

R=0.95


Weig

ht

norm

aliz

ed

Length

aw

ard

ed

specific elements


than n-grams

awarding

length yields

higher results

awarding

length yields

higher results

F=0.76

P=0.64

R=0.95

• SPEC slightly worse than ML-Ask

(F = 0.79, P = 0.8, R = 0.78)

SPEC > ML-Ask

fully automatic handcrafted


Weig

ht

norm

aliz

ed

Length

aw

ard

ed

specific elements


than n-grams

awarding

length yields

higher results

awarding

length yields

higher results

F=0.76

P=0.64

R=0.95

• SPEC slightly worse than ML-Ask

(F = 0.79, P = 0.8, R = 0.78)

SPEC > ML-Ask

fully automatic handcrafted

• More efficient (user does

nothing)

• Applicable to other languages

• Can point out non-emotive el.

RESULTS AND DISCUSSION

Examples of extracted

Patterns (Tokenized)

Emotive Non-emotive

freq. example pattern freq. example pattern

14 、＊た 11 い＊。

12 で 8 し＊。11 ん＊。 7 です。

11 と 6 は＊です11 ー 6 まし＊。10 、＊た＊。 5 ました。

9 、＊よ 5 ます9 、＊ん 5 い

8 し 4 です＊。7 ない 3 この＊は＊。7 ！ 3 は＊です。6 ん＊よ 3 て＊ます6 、＊だ 3 が＊た。

6 ちゃ 3 美味しい6 よ。 3 た。5 だ＊。 2 た＊、＊。5 に＊よ 2 せ5 が＊よ 2 か

5 ん 2 さ

Emotive

- Casual wording

- Prolongation

marks

- SFPs

- Subject

particles

Non-emotive

- Official forms

(desu-masu)

- More “periods”

- No exclamation

marks, etc.

RESULTS AND DISCUSSION

EXAMPLE SENTENCES

Example 1.

メガネ、そこにあったんだよ。Megane, soko ni atta n da yo .

(The glasses were over there!)

Example 2.

ううん、舞台が見えないよ。Uun, butai ga mienai yo .

(Ooh, I cannot see the stage!)

Example 3.

ああ、おなかがすいたよ。Aa, onaka ga suita yo .

(Ohh, I’m so hungry)

><0

Example 4.

高額なためです。Kougaku na tame desu.

Due to high cost.

Example 5.

きれいな海ですKirei na umi desu

This is a beautiful sea

Example 6.

今日は雪が降っています.

Kyou wa yuki ga futte imasu.

It is snowing today.

:-|

CONCLUSIONS & FUTURE WORK

Presented research on extracting emotive patterns.

Used SPEC - a method for automatic extraction of patterns from sentences.

Extracted the patterns from a set of emotive and non-emotive sentences.

Classified sentences (test data) with those patterns.

Compared different preprocessing techniques (tokenization, POS, token-POS).

The best results obtained patterns with both tokens and POS (F-score = 76%, Precision = 64%, Recall 95%).

Results for only POS were the lowest. This means the algorithm works better on less abstracted data.

The results of SPEC were compared to ML-Ask affect analysis system. ML-Ask achieved better Precision, but lower Recall. However, SPEC is fully automatic and thus more efficient and language independent.

Many of the automatically extracted patterns appear in handcrafted databases of ML-Ask, which suggests it could be possible to improve ML-Ask performance by extracting additional patterns with SPEC.

In the future we’ll try to quantify the correlation between emotive-subjective-emotional

THANK YOU FOR YOUR ATTENTION!

Kitami Institute of Technology

ptaszynski＠ieee.org

http://orion.cs.kitami-it.ac.jp/tipwiki/michal

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