Express Emoticons Choice Method for Smooth Communication of e-Business
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Express Emoticons Choice Method
for Smooth Communication of e-Business
Nobuo Suzuki and Kazuhiko Tsuda
Graduate School of Buisiness Sciences, University of Tsukuba
Otsuka 3-29-1, Bunkyo, Tokyo 112-0012, Japan
{nobuo, tsuda}@gssm.otuka.tsukuba.ac.jp
Abstract. For the business communication by email with cellular phones, it has
an important weak point. That is to hard to tell to be utterance speed and the
pitch of sounds involved in the sentences, because it communicate by letters
only. Emoticons are often used to make up for this weak point. This paper
describes techniques to predict emotions of sentences in Japanese emails and
give an emoticon to end of a sentence automatically. This is achieved by
learning information of emotions with emoticons used and analyzing the text of
email with cellular phone by collecting and analyzing our corpus of emails. We
also examined consistency evaluation with real email sentences input by
cellular phones and emoticons automatically generated by this technique. We
could get correct answer rate of 87.7%.
Keywords: Prediction of emotions, Morpheme analysis, Emoticons.
1 Introduction
We often use email for our business communications. In such situation, it is difficult
to express emotions because we usually use letters only. Therefore it is common to
express emotions by using emoticons. Recently, the cellular phones connected to the
Internet become the daily tools and most familiar input tools to the Internet. We use
emoticons with cellular phones more frequently than normal PCs. So, we collected
and analyzed Japanese email sentences input from cellular phones. Fig.1 shows
examples of sentences with emoticons input from cellular phones.
・
・
How about this? o(^-^)o
The other person was in Osaka, and I was in Saitama (>_Express Emoticons Choice Method for Smooth Communication of e-Business 297
predict the emotions of email text in cellular phones and give an emoticon to the end
of a sentence automatically.
Fig.2 shows our method for generating best emoticons to express emotions.
Corpus Data Microsoft IME
Emotions
of Plutchik
Emotions Part Emoticons
of Speech (@_@)
Emotion Dictionary Emoticon Dictionary
Input Detection of Output Emoticons
Sentences Words of Emotions (^_^)Y
Fig. 2. The processing model of this method
2 Related Works
Various classification methods of emotions based on this study were suggested so far
[1]
. Woodworth said he could classify emotions to six basic emotions, and Schlossberg
expanded these six emotions and suggested a three-dimensional model of emotions.
Plutchik also made a criterion of basic emotions clear and defined eight emotions.
This definition suggests the opposite meaning of emotions and a three-dimensional
strength model based on corpus, so it is finer model than others.
A lot of automatic distinction methods of emotions in the various media with
computers have been studied. Kanoh applied information of emotions to expressions
in robots [2], and Matsumoto suggested emotions recognition technique by images and
sounds [3]. Keila also examined emotions understanding method of emails as
technique for customer’s problem discovery [4].
Kort analyzed the emotion transition for learning situation[7]. They proposed an
interesting four phase emotions transition model. This is important point for the
smooth communication.
In addition, Nakamura suggested the technique with neural network for emotions
distinction of emoticons in dialogues [5]. This is one of the methods for understanding
the meaning of emoticons.
In these works, they didn’t examine about automatic emoticons grants technique
for the smooth communication.298 N. Suzuki and K. Tsuda
3 Classification Emotions
At first, it is important to define classifications of human emotions itself that decides
what kind of emotions emoticons express. Many classification methods of emotions
were suggested so far. We use a classification method based on eight basic emotions
of Plutchik.R which can express various emotions by the following reasons [1]. Fig. 3
shows the eight basic emotions and its opposite meanings.
(1) It expresses the strength of emotions.
(2) It expresses the opposite meaning of emotions.
(3) It defines actions with emotions and relation of the actions and the emotions.
(4) It defines the mixture of basic emotions and can explain various emotions.
Table 1 shows the relation of feelings with Plutchik method and words of
emotions. We express emotions of emoticons by these words of emotions and
additional ones described in next clause.
Acceptance
Joy Fear
Anticipation Surprise
Anger Sadness
Disgust
Fig. 3. The processing model of this method
Table 1. Emotions classification of Plutchik
Basic Emotions (Strong) ← Strength → (Weak)
Acceptance Lo ve, Good will, Trust, Generosity, Acceptance
Fear Grief, , Worry, Sorrow, Discouragement, Sadness
Disgust Hatred, Hate, Antipathy, Disgust
Anger Anger, Rage, Fury, Indignation, Hostility
Anticipation Anticipation, Expectation, Caution, Curiosity, Attention
Joy Pride, Joy, Satisfaction, Pleasure, Peace
4 Definition of Emoticons That Show Emotions
We define emoticons corresponding to the words of emotions that were showed
before. The emoticons to use were picked up representative emoticons equivalent toExpress Emoticons Choice Method for Smooth Communication of e-Business 299
each emotion words by one or more questionnaire from 172 emoticons defined in
Microsoft IME 2003.
When we simply relate IME to Plutchik’s words of emotions, it is concerned about
falling off emoticons and words of emotions with high frequency using. Therefore,
we pull out and add words of emotions which are used in emails of cellular phones
and doesn’t appear in Plutchik’s words of emotions from our collection of email data.
We also add words which are in Plutchik’s words of emotions and short in IME. We
show additional words of emotions in Table 2.
We picked up pairs of emoticons and words of emotions as above. Table 3 shows
parts of these. We store them in our system as “Emoticon Dictionary”.
Table 2. The additional words of emotions
Irritating
Ridiculous
Apology
Sleepy
Tired
Greeting
Normal farewell
Sad farewell
Request
Fear
Love
Acceptance
Table 3. The part of “Emoticon Dictionary”
Emoticons Words of emotions
(^_^)/ Greeting
(>_300 N. Suzuki and K. Tsuda
technique can handle it more precisely than the method of searching all strings. We
use ChaSen[2] for morpheme analysis.
We chose the parts of speech to express emotions from morphemes defined in
ChaSen. We call this "Emotions parts of speech". Table 4 shows a list of these.
Table 4. A list of Emotions parts of speech (in Japanese)
Emotions part of speech Examples of words Number of words
in the dictionary
of ChaSen
Noun, Changed connection of “Sa” 愛着 (Attachment), 12,041
ひと安心 (Settled)
Noun, Stem of adjective verb 安易 (Easygoing), 3,313
だめ (No good)
Noun, Stem of adjective for “Nai” 申し訳 (Excuse), 42
仕方 (No choice)
Adjective, Adjective・Step ”i” 哀しい (Sad), 654
楽しい (Fun)
Adjective, Unchanged type かっこいい (Cool), 8
きもちいい (Comfortable)
The number of words in total 16,058
Next, we extracted words for each emotion part of speech from 2,218 Japanese
sentences input by cellular phones which we actually collected. We decided what kind
of emotions these words expressed by questionnaires and built "Emotion Dictionary"
such as Table 5.
Table 5. The part of "Emotion Dictionary"
Parts of Speech Emotion words Emotions
Noun, Changed connection of “Sa” お願い (Request) Request
お祝い (Celebration) Pleasure
Noun, Stem of adjective verb 不安 (Worry) Perplexity
不利 (Disadvantageous) Sadness
Noun, Stem of adjective for “Nai” 仕方 (No choice) Sadness
申し訳 (Excuse) Apology
Adjective, Step ”i” よろしく (Best regards) Greeting
あいくるしい (Lovely) Love
Adjective, Unchanged type かっこいい (Cool) Pride
6 Emoticon Automatic Grant Technique
This chapter describes the technique to give an emoticon to express emotions for a
sentence in an email of cellular phones by using Emoticon dictionary and Emotion
Dictionary showed in last chapter. This technique is carried out by the following
procedures.Express Emoticons Choice Method for Smooth Communication of e-Business 301
(1) Input one sentence.
(2) Get the emotion part of speech at the end of the sentence by morpheme analysis.
(Because it is often that cellular phone email sentences have an emoticon in the
last of a sentence, we also grant an emoticon to the end of a sentence.)
(3) Get an emotion word from Emotion dictionary using an emotion part of speech
and real words as keys.
(4) Get an emoticon for that emotion word from Emoticon Dictionary.
(5) When emotions part of speech that we get is "Noun - Stem of adjective for Nai”,
check whether there are "auxiliary verb - special Nai" just after that. If it gets
one, it defines an emoticon that shows the opposite meaning of emotions at (4).
(It can find opposite emotions by emotion classification method of Plutchik.)
(6) Output an emoticon which is converted from the punctuation mark at end of an
input sentence.
7 Evaluation Experiment
We compared the real emoticons with emoticons acquired by this technique for 65
sentences with emoticons input by cellular phones. When the emoticon did not
completely accord, we assumed it was correct answer if correct semantically. Table 6
shows examples of output sentences and Table 7 shows our results.
Table 6. Examples of output sentences
Input sentences Output emoticons Decision
Anyone has same job know this, please m(__)m Good
reply to me. m(__)m
How about cookies or cakes for his (^_^)v Good
birthday? (^ﾛ^)
I received an application, but I was (~_~) Good
worried about the expence. (*_*)
I think the jobs that a high school student (^o^)ノ Good
can work are the most reliable one. (^_^;
I was happy but … (^_^) (T_T) No Good
If you are worried about it, please (~_~;) No Good
examine it by books of cats. V(^-^)V
Table 7. Evaluation results
Number of sentences Ratio
Correct 57 87.7%
Wrong 8 12.3%
Total 65 100.0%
As a result of evaluation, the correct answer ratio was 87.7% and our method is
almost effective enough. It was 12% wrong answer ratio in our evaluation. We
describe some reasons of them below.302 N. Suzuki and K. Tsuda
(1) It cannot understand a conjunctive particle.
It is ambiguous meaning such as “ けどね ” with information only for one
sentence, and difficult to distinct emotions if it has emoticons even human being.
For example, “ 楽しかったけどね (^-^)”. (“I was happy, but…” in English.)
(2) Morphemes out of the range
For example, case of the sentence “ もし心配なら猫の本とか見て調べてみて
下さい V(^-^)V” (“If you are worried about it, please examine it by books of
心配
cats.” In English), it chose an emoticon for “ ”, but the correct choice is one
調べてみて下さい
for “ ”(Its morpheme is Verb – five steps, “Ra” line special).
We are able to handle this problem by extension of morphemes to intend for.
8 Conclusion
In this paper, we defines the emotions classification with emoticons and proposed the
technique to grant an emoticon which express emotions of it at end of email sentence
by input from cellular phones.
We were able to confirm the correct answer rate of 87.7% as a result of evaluation
experiment and the effectiveness of this method.
We plan to study to understand of emotions by context for ambiguous expressions
in future. We think it is important evaluation point that using this method in real
world.
References
1. Fukui Y.: Psychology of emotions, Kawashima Publish (1990)
2. Gotoh M, Kanoh M., Kato S., Kunitachi T., Itoh H.: Face Generation Using Emotional
Regions for Sensibility Robot, Transactions of the Japan Society for Artificial Intelligence
(2006) Vol.21 No.1 pp.55-62
3. Matsumoto S., Yamaguchi T., Komatani K., Ogata T., Okuno H.: Emotion recognition by
integration face image information and sound information for using in robots, Proceedings
of 22th The Robotics Society of Japan Conventions (2004) 3D14
4. Keila P.S., Skillicorn D.B: Detecting unusual and deceptive communication in email,
External technical report, School of Computing, Queen’s University (2005)
5. Nakamura J., Ikeda T., Inui N., Kotani Y.: Learning Face Mark for Natural Language
Dialogue System, Natural Language Processing Study Report of Information Processing
Society (2003) No.154-24
6. Matsumoto H.: A morpheme analysis system “ChaSen”, Information Processing (2000)
Vol.41 No.11
7. Kort B., Reilly R., Picard R.: An Affective Model of Interplay Between Emotions and
Learning: Reengineering Educational Pedagogy – Building a Learning Companion,
Proceedings of International Conference on Advanced Learning Technologies (2001)You can also read
