International Journal of Advanced Intelligence
Volume 8, Number 1, pp.99-116, May, 2016.
c AIA International Advanced Information Institute
⃝
Developing a Japanese Adverb-Emotion Corpus to Investigate
the Effect of Adverbs in Japanese Sentence Emotion
Classification
Yan Sun† Fuji Ren† , Xin Kang‡†
† Faculty of Engineering, Tokushima University
2-1, Minamijyousanjima-cho, Tokushima 770-8506 Japan
[email protected], [email protected], [email protected]
‡ Electronics and Information, Tongji University
Tongji University, 1239 Siping Road, Shanghai, P.R. China
[email protected]
Changqin Quan
Graduate School of System Informatics, Kobe University
1-1 Rokkodai, Nada, Kobe 657-8501, Japan
[email protected]
Received (10 June 2015)
Revised (10 Sep. 2015)
Recognizing emotions from text has become a popular direction in natural language processing. Previous works have extensively studied words as a significant feature, but very
few have explored the specialty of Part-of-Speech in words. In Japanese, adverbs are frequently used in both written and spoken language, and could remarkably impact emotion
expressions based on our observation. In this paper, we focus on the function of Japanese
adverbs in emotion expression and develop a Japanese adverb-emotion corpus with eight
emotion labels annotated in adverbs and sentences. New adverb categories and adverbemotion rules are extracted based on their emotional functions, and an adverb-emotion
lexicon is constructed based on the statistics of emotion annotations. We examine the
function of these adverb-emotion features by employing them in the sentence emotion
classification in our corpus. The experiment result shows promising improvement in the
Accuracy, Precision, Recall, and F1 scores, compared to the word features. Moreover, we
test these adverb-emotion features on a new test set, and demonstrate the generalizability
of these features in the real texts.
Keywords: Adverb emotion; emotion feature; emotion corpus; emotion classification.
1. Introduction
With the quick development of information processing, emotion prediction and emotion analysis have been widely used in human-computer interaction, affective robot
development, and mental diseases diagnosis. The number of applications based on
automatic emotion classification has been growing fast in these domains.
For text emotion prediction, researchers have investigated different language
features. Such studies include the conjunction based text emotion prediction 1 ,
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Y. Sun, F. Ren, C. Quan
the degree words based emotion analysis 2 , the emotional words based emotion
recognition 3 , and the sentence structure based emotion prediction 4 . However, the
function of adverbs in text emotion prediction has only been mentioned in very few
related works, but has never been thoroughly studied.
The linguistic study of Japanese language suggests that the frequent usage of
adverbs can indicate subtle changes in the writer’s mind 5 . The Japanese adverbs
have been considered as the direct modification of emotion expressions in the works
of Mika et al. 6 and Matsumoto et al. 7 Adverbs also play an import role in constructing Japanese sentences in machine translation, as suggested by the work of
Mika et al. 8 We investigate the adverb usage in modern Japanese language, and
depict the statistics of adverb usages based on the Dictionary of Japanese Language
9
and the Reality of Discourse Language 10 , respectively in Table 1. The observation
of adverb counts for 1.25% among the observation of all kinds of words, which is
only below noun and verb. In spoken Japanese language, the adverb usage is even
more frequent, which counts 12.2% of all kinds of words. All these statistics have
proved the importance of adverbs in the Japanese language. These research on adverbs and emotions provide us an intuition of exploring the function of adverbs in
emotion expression and employing these adverbs as features in the sentence emotion
prediction.
Table 1. The statistics of adverbs in Japanese.
Part-of-Speech
Noun
Verb
Adverb
Adjective 1
Adjective 2
Conjunction
Particle
Dict of Japanese Language
73.19%
5.72%
1.25%
1.00%
1.09%
0.06%
0.07%
Real. of Discourse Language
40.90%
24.40%
12.2%
5.40%
2.4%
3.8%
–
In this paper, we focus on the function of Japanese adverbs in emotion expression, and firstly build a Japanese adverb-emotion corpus. Totally 3,864 Japanese
sentences with 542 distinct adverbs have been collected, with the similar corpus collection method developed by Wiebe et al. 11 , Quan et al. 12 and, Yamamoto et al.
13
. For each sentence, we manually annotate the emotion label from eight emotion
categories, including Expection, Joy, Love, Surprise, Anxiety, Sorrow, Anger, and
Hate, and specify the relevant emotion intensities, ranging from 0.1 to 1.0.
We find the Japanese adverbs have different functions in text emotion expressions, by analyzing the adverb usages in our Japanese adverb-emotion corpus.
For example, some adverbs can directly express emotions, while other adverbs
Developing a Japanese Adverb-Emotion Corpus
101
may change the intensity of emotion expressions. We divide the Japanese adverbs
into eight categories, according to their emotion expression functions. A Japanese
adverb-emotion lexicon is built, based on the statistics of adverb emotions in our
corpus. We summarize the adverb usage under different linguistic conditions and
within different emotion expressions in the Japanese sentences, and build a set of
adverb-emotion rules for specifying the text emotions.
To examine the function of adverbs in emotion expressions, we performance an
experiment of sentence emotion classification based on the adverb categories, the
adverb-emotion lexicon, and the adverb-emotion rules. The Support Vector Machines (SVM) algorithm is employed to train several emotion classifiers based on
different adverb-emotion features on 2,612 sentences in our adverb-emotion corpus.
We evaluate the emotion classification results on the rest 652 sentences, and compare the results rendered by different features. Our experiment demonstrates the
effectiveness of these adverb-emotion features for text emotion classification. To
examine the generality of adverb-emotion features, we train a classifier based on
the whole corpus, and predict the sentence emotions on a testing set of 584 new
sentences extracted from the works of Wiebe et al. 11 and Mayu et al. 13 . This experiment suggests that the adverb-emotion features are also applicable for predicting
emotions in the unseen texts.
The remaining part of this paper is organized as follows. Section 2 presents the
construction of a Japanese adverb-emotion corpus. Section 3 describes the extraction of the adverb categories, the adverb-emotion lexicon, and the adverb-emotion
rules. We illustrate the experiment and result of sentence emotion classification in
section 4, and conclude this study with closing remarks and future directions in
section 5.
2. Construction of an adverb-emotion corpus
Emotion corpus has been considered the foundation of text emotion analysis, and
many researches have been performed for constructing such corpus. Wiebe et al. 11
constructed a fine-grained opinion and emotion corpus (MPQA) in English. Quan
and Ren 12 proposed a multi-level blog emotion corpus for analyzing emotional
expressions in Chinese blog articles. However, emotion corpus resource in Japanese
is very limited. Based on the researches of Yamamoto et al. 13 and Matsumoto et
al. 14 , in this study we propose the construction of an emotion corpus in Japanese
language, and specify the emotion expressions in Japanese adverbs in this corpus.
To construct such an adverb-emotion corpus, we firstly identify the most commonly used Japanese adverbs from the Japanese dictionaries 15,16,17 . The representative sentences are then collected for each of these identified adverbs. Table
2 shows the basic statistics in our Japanese adverb-emotion corpus. Totally 542
distinct adverbs have been included, with 4,384 times of observation of these adverbs. Within all collected adverbs, 206 adverb words are found with emotions,
and the observation of these emotional adverbs counts 1,205 times, which takes
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27.72% of the total adverb observations in the corpus. The whole corpus contains
3,864 Japanese sentences, among which 1,108 sentences (counting 33.95%) consist
emotional adverbs.
Table 2. The statistics of Japanese adverb-emotion corpus.
Adverb
Sentence
Number of adverbs
Number of emotional adverbs
Occurrence of adverbs
Occurrence of emotional adverbs
Number of sentences
Number of sentences with emotions
Number of sentences without emotions
Number of sentences with emotional adverbs
542
206
4,394
1,205
3,864
3,264
600
1,108
100.00%
38.01%
100.00%
27.42%
100.00%
84.47%
15.52%
33.95%
The adverbs and sentences in this corpus have been manually annotated with
the emotion labels based on the eight emotion categories and the relevant emotion
intensities in the range of 0.1 to 1.0.
3. Extraction of adverb-emotion features
3.1. Adverb-emotion feature categories and classification schema
We divide the Japanese adverbs into eight categories according to their functions
in the text emotion expression. Table 3 illustrates the definition of each adverb
category and demonstrates specific examples for these categories.
Within these adverb categories, Type 0 adverbs have no effect to the text emotion expression or the emotion intensity. Type 1 to Type 3 adverbs are emotional
words, in which Type 1 and Type 2 adverbs can strengthen or weaken the intensity
of emotions, and the Type 3 adverbs directly affect the sentence emotion expressions. Type 4 to Type 7 adverbs are not emotional words, but could indirectly
affect the sentence emotions. Independently the Type 4 adverbs do not contain any
emotion, but in an emotional context they tend to express the same emotion as
the relevant sentences. Type 5 adverbs tend to express different emotions against
the sentence emotions in an emotional context. With some particular verbs in the
context, Type 6 adverbs could generate positive emotions. And Type 7 adverbs are
able to change the sentence emotions.
3.2. Construction of an adverb-emotion lexicon
By analyzing the adverb categories, the emotion annotations in adverbs, and the
adverb usage types in our corpus, we construct an adverb-emotion lexicon with
Developing a Japanese Adverb-Emotion Corpus
103
Table 3. Adverb features and feature frequencies.
ID
0
2
Category
No effect on emotion or emotion intensity
Strengthen the emotion intensity
Weaken the emotion intensity
3
As an emotional word
4
Only expression the same
emotion as its emotional context
Express different emotions
against the emotional context
Express positive emotions in
an emotional context
Change emotions in an emotion context
1
5
6
7
Freq.
184
Example 1
ぎいぎい
Guy Guy
とても
very
少し
a little
にこにこ
smiling
別れ別れに
individually
214
30
206
9
Example 2
みしみし
Mishimishi
非常に
extremely
やや
somewhat
うんざり
tediously
なよなよ
weakly
1
どうにか
いつの間に
somehow
wondering when
せっせと diligently, conscientiously
1
さも seem
4
totally 687 adverbs. We demonstrate the statistics of this adverb-emotion lexicon
in Table 4 and Table 5.
Table 4. Count of adverb usage types.
Usage type count
Occurrence count
1
418
2
97
3
25
4
2
Table 5. Count of adverbs with emotions.
Emotion
Count
Exp.
6
Joy
22
Love
46
Surp.
18
Anxiety
73
Sorrow
17
Anger
8
Hate
63
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Y. Sun, F. Ren, C. Quan
3.3. Extraction of adverb-emotion rules
By analyzing the emotion expressions relevant to adverbs in the adverb-emotion corpus, we find that the adverb categories are closely related to the sentence context,
and these contexts are closely related to the semantic meanings of these adverbs. In
most cases, the adverbs have single semantic meanings, which correspond to sigle
adverb categories. For a few adverbs which contain multiple semantic meanings,
we could specify their categories according to the context information. We summarize the adverb-emotion rules based on the sentences in Japanese adverb-emotion
corpus, which contain similar adverb categories. Totally 13 adverb-emotion rules
are summarized as shown in Table 6. We explain these rules with specific examples
from the corpus.
We illustrate the detailed annotation of emotion labels and emotion intensities
to both adverbs and sentences in Table 13 in Appendix A. For each of the eight
adverb categories, we explain the context constraints as rules, and demonstrate
rules with specific examples.
4. Experiment
We examine the function of the adverbs in emotion expression by employing them
in the sentence emotion classification task, based on our Japanese adverb-emotion
corpus. Words are considered as the basic features in this experiment, while adverb
categories, adverb-emotion lexicon, and adverb-emotion rules are incorporated as
the additional emotion features. To examine the generalizability of these features, we
evaluate the trained emotion classifiers on an open test corpus of 584 new Japanese
sentences with adverbs.
4.1. Evaluate adverb-emotion features in closed test set
The Japanese adverb-emotion corpus consists totally 3,264 Japanese sentences. Because we focus on the function of Japanese adverbs for emotion expression, sentences without adverbs are not included in our study. Words are extracted with the
Japanese word segment package ChaSen 18 , with the stop words such as “に” and
“と” removed.
We train the multi-class SVM classifier for Japanese sentence emotion classification, based on eight basic emotion categories, including Expection, Joy, Love, Surprise, Anxiety, Sorrow, Anger, and Hate. The classifier is trained on 2,612 sentences
in the adverb-emotion corpus, and evaluated on the rest 652 sentences based on
the Precision, Recall, and F1 scores for each emotion category. The macro-average
of these evaluations as well as the Accuracy score are calculated for evaluating the
overall performance.
We employ the words as basic features in sentence emotion. To examine the function of adverbs in sentence emotion expression, we compare the classification results
based on all words except adverbs in experiment E1.1 and the classification results
Developing a Japanese Adverb-Emotion Corpus
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Table 6. The adverb-emotion rules.
Adverb
ぐずぐず
lounged
Context
がんがん
apounding
Statement pattern
Modified predicate
Special words
たまたま
by chance
せっせと
be diligent and
consicientious
すんなり
smoothly
ふんわり
fluffy
よく
unexpectedly
ちょっと
just
あまり
so
一体
on earth
思い切って
daringly
Semantic
Emotional word
Synonym
Exclusion
Back and forth of
the adverb
Modified
Part-of-Speech
Position and negation
The end of a statement
Tense of modified
predicate
Examples
する do or verb
言う speak
頭 が がんがん する
Head is pounding
鐘 | 火 | 音 | たたく 響く
Bell|Tues|Sound clap or
echoing
Semantic 1: sometimes,
occasionally
Semantic 2: just at that
time; by chance
With an emotional word
Without an emotional
word
Part of the body
Type
3 Hate
3 Anger
adv + float
Other conditions
Back and forth of the
adverb is the antonym
Emotional adjective
verb|noun|verb+する
Position
Negation
The end of a statement
is guess or question
Past tense verb
0
3 Love
3 Surprise
3 Anxiety
0
0
5
1
6
3 Love
“2”
“0”
1
2
1
3 Love
based all words in experiment E1.2. We add the adverb category, the adverb-emotion
lexicon, and adverb-emotion rules as the extra features respectively for experiment
E1.3, E1.4, and E1.5, and examine the corresponding sentence emotion classification
results. Tables 7 to 9 depict the evaluation of emotion classification results based on
Precision, Recall, and F1 scores for each emotion label. And a further comparison
of different adverb-emotion features for sentence emotion classification, based on
the Accuracy and the macro-averaged Precision, Recall, and F1 scores, is shown in
Figure 1.
Our experiment results suggest that incorporating adverbs as the word feature
is important for training a sentence emotion classifier. The classification Accuracy
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Y. Sun, F. Ren, C. Quan
Table 7. Precision scores of sentence emotion classification in the close test set.
Emotion
Expection
Joy
Love
Surprise
Anxiety
Sorrow
Anger
Hate
E1.1
100.00
47.85
57.78
39.62
51.16
55.40
40.00
25.00
E1.2
66.67
54.76
61.84
60.66
53.33
60.00
62.96
68.75
E1.3
50.00
56.12
61.54
58.82
56.45
61.47
66.67
66.67
E1.4
50.00
58.33
58.90
65.43
55.71
60.71
73.08
70.83
E1.5
75.00
60.07
60.76
81.69
60.87
70.30
70.37
67.86
Table 8. Recall scores of sentence emotion classification in the close test set.
Emotion
Expectation
Joy
Love
Surprise
Anxiety
Sorrow
Anger
Hate
E1.1
7.14
70.05
66.67
25.61
27.16
57.04
24.00
6.67
E1.2
10.00
77.78
65.28
43.53
37.21
56.56
45.95
47.83
E1.3
5.00
79.71
66.67
47.06
40.70
54.92
43.24
43.48
E1.4
5.00
74.40
59.72
62.35
45.35
55.74
51.35
73.91
E1.5
15.00
79.23
66.67
68.24
48.84
58.20
51.35
82.61
Table 9. F1 scores of sentence emotion classification in the close test set.
Emotion
Expectation
Joy
Love
Surprise
Anxiety
Sorrow
Anger
Hate
E1.1
13.33
56.86
61.90
31.11
35.48
56.20
30.00
10.53
E1.2
17.39
64.27
63.51
50.68
43.84
58.23
53.13
56.41
E1.3
9.09
65.87
64.00
52.29
47.30
58.01
52.46
52.63
E1.4
9.09
65.39
59.31
63.86
50.00
58.12
60.32
72.34
E1.5
25.00
68.33
63.58
74.36
54.19
63.68
59.38
74.51
in experiment E1.2 (57.67%) is significantly higher than the classification Accuracy
in experiment E1.1 (50.00%). The emotion classifier in E1.2 has achieved significant
increments in macro-averaged Precision, Recall, and F1 scores, by 9.02%, 12.47%,
Developing a Japanese Adverb-Emotion Corpus
70
60
50
40
30
20
10
0 E1.1
E1.2
E1.3
Accuracy
Macro-Precision
E1.4
107
E1.5
Macro-Recall
Macro-F1
Fig. 1. The macro-averaged Precision, Recall, F1, and Accuracy of sentence emotion classification
with different adverb-emotion features in the close test set.
and 14.00%, respectively.
The adverb-emotion categories provide basic information of adverb functions in
emotion expression. By extending word features with the adverb-emotion categories
for sentence emotion classification, we have observed a classification Accuracy increment of 0.92% in experiment E1.3, compared to experiment E1.2. The reason
for classification results on the emotion labels not significantly improved in E1.3 is
mainly because that the adverb-emotion category reflects the adverb function on
the sentence emotion intensities rather than on the sentence emotion labels.
The adverb-emotion lexicon provides detailed information of the direct emotion
expressions in adverbs. Compared to the experiments E1.2 and E1.3, the SVM classifier trained on the adverb-emotion lexicon features has shown significant improvement in sentence emotion classification. The classification Accuracy in experiment
E1.4 is 2.76% and 1.84% higher than the Accuracies in E1.2 and E1.3. By analyzing the classification results for separate emotion labels, we find that the Precision
scores for Expectation, Joy, Surprise, Anger, and Hate and the Recall scores for Surprise, Anxiety, Anger, and Hate are also significantly increased compared to E1.2
and E1.3. The F1 scores for all emotion labels are improved in experiment E1.4.
The function of adverbs in emotion expression varies in different context. Unlike the adverb-emotion categories and the adverb-emotion lexicon, adverb-emotion
rules could provide context sensitive information for sentence emotion classification.
Among all experiments on the close test set, experiment E1.5 achieves the best performance. The SVM classifier renders an Accuracy score of 65.03%, which is higher
than those in the previous experiments. We observe significant improvements in
Precision, Recall, and F1 scores for almost all emotion labels in this experiment.
Compared to experiment E1.4, the macro-averaged Precision, Recall, and F1 scores
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Y. Sun, F. Ren, C. Quan
are increased by 6.47%, 5.29%, and 5.57%, respectively.
4.2. Evaluate adverb-emotion features in open test set
The results in previous experiment suggest our adverb-emotion features are effective
in predicting sentence emotions, in the closed set of examples based on Japanese
adverb-emotion corpus. To further examine the generality of these features, we
perform the emotion classification on a new test set of 584 Japanese sentences.
Emotions in these sentences are annotated by following the same criteria as in
the Japanese adverb-emotion corpus. The same word segmentation and stop word
filtering procedures are performed on these sentences to get similar word features in
the closed test set. We employ the combination of words with the adverb category,
adverb-emotion lexicon, and adverb-emotion rules respectively as features to train
an emotion classifier based on the Japanese adverb-emotion corpus, and evaluate
the classification results on the open test set. The experiments are denoted as E2.1,
E2.2, and E2.3, respectively, and the corresponding evaluations in Precision, Recall,
and F1 are shown in Tables 10 to 12. Figure 2 compares the Accuracies and the
macro-averaged Precision, Recall, and F1 scores for these experiments.
Table 10. Precision scores of sentence emotion classification in the open test set.
Emotion
Expectation
Joy
Love
Surprise
Anxiety
Sorrow
Anger
Hate
E2.1
40.91
60.66
60.17
70.00
51.76
81.25
0.00
82.00
E2.2
50.00
69.64
60.43
72.22
65.92
71.43
66.67
82.35
E3.3
47.37
67.21
62.60
73.68
65.52
80.95
88.89
94.52
Within all experiments on the open test set, the SVM classifier trained on the
adverb-emotion rule feature in E2.3 achieves the highest average performance. For
584 sentences, the classifier correctly predicts emotions for 401 sentences, rendering
an Accuracy score of 68.66%, which is 11.47% and 1.88% higher than the Accuracy
scores achieved by E2.1 and E2.2.
The classifier trained in E2.3 also gets the highest Precision scores for Love,
Surprise, Sorrow, Anger, and Hate within all the open test experiments. For Joy,
Anxiety, Expectation, the Precision scores rank the second, with only small differences to the best scores. The highest Recalls are achieved for Expectation, Joy,
Surprise, Anxiety, Sorrow, and Hate by the emotion classifier in E2.3, within the
open test experiments. For Love and Anger, this classifier also generates high Re-
Developing a Japanese Adverb-Emotion Corpus
109
Table 11. Recall scores of sentence emotion classification in the open test set.
Emotion
Expectation
Joy
Love
Surprise
Anxiety
Sorrow
Anger
Hate
E2.1
60.00
45.12
66.98
35.00
83.05
34.21
0.00
34.75
E2.2
60.00
47.56
79.25
65.00
83.05
39.47
30.77
59.32
E3.3
60.00
50.00
77.36
70.00
85.88
44.74
61.54
58.47
Table 12. F1 scores of sentence emotion classification in the open test set.
Emotion
Expectation
Joy
Love
Surprise
Anxiety
Sorrow
Anger
Hate
E2.1
48.65
51.75
63.39
46.67
63.77
48.15
0.00
48.81
E2.2
54.55
56.52
68.57
68.42
73.50
50.85
42.11
68.97
E3.3
52.94
57.34
69.20
71.79
74.33
57.63
72.73
72.25
call predictions, which are very close to the corresponding highest Recall predictions
from experiment E2.2. The F1 scores in experiment E2.3 turn to be the highest for
all emotion labels expect the Expectation, which is a little lower than that achieved
in experiment E2.2.
Our experiment results demonstrate that the adverb-emotion rules are effective
features for sentence emotion prediction. By examining the evaluation scores for
separate emotion labels in E2.3, we find that the Precision scores are higher than
90% for Expectation, Anxiety, Sorrow, and Hate, and are between 70% to 90% for
Joy, Love, Surprise, and Anger, which have been very promising for this task. The
Recall scores for Surprise, Anger, and Hate reach up to higher than 90%, and Recall
scores for Expectation, Joy, Love, Anxiety, and Sorrow are between 60% to 90%.
For all emotion labels, the F1 scores are over 80%. The major classification errors
are caused by the segmentation mistakes and some unrecognized language features,
such as the verb tense which has not been considered in the reported work.
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Y. Sun, F. Ren, C. Quan
80
70
60
50
40
30
20
10
0
E2.1
E2.2
Accuracy
Macro-Precision
E2.3
Macro-Recall
Macro-F1
Fig. 2. The macro-averaged Precision, Recall, F1, and Accuracy of sentence emotion classification
with different adverb-emotion features in the open test set.
5. Conclusion
The studies in Japanese language have demonstrated that a large part of Japanese
adverbs can affect subtle changes in the human emotions or directly express the
human emotions. In this paper, we focus on the function of Japanese adverbs in the
sentence emotion expression. A Japanese adverb-emotion corpus has been firstly
constructed, with the major Japanese adverbs contained in 3.864 sentences. Emotion labels representing eight basic emotion categories, i.e., Expectation, Joy, Love,
Surprise, Anxiety, Sorrow, Anger, and Hate, have been labeled to the adverbs and
sentences in the corpus. Because adverbs could affect sentence emotions in different
manners, such as directly expressing emotions in sentences or increasing the sentence
emotion intensities, we have defined eight categories of adverbs to specify their function in sentence emotion expressions. A Japanese adverb-emotion lexicon has been
built based on the statistics of adverb emotions in the corpus. We also summarize
the adverb-emotion functions under different linguistic contexts, and build a set of
adverb-emotion rules for generating more specific features for text emotion prediction. By employing the adverb-emotion category, the adverb-emotion lexicon, and
the adverb-emotion rules as features in a multi-class SVM classification algorithm,
we examine the function of Japanese adverbs in the sentence emotion classification,
based on our Japanese adverb-emotion corpus and an open test set of 584 Japanese
sentences. Experiment results demonstrate the effectiveness of these adverb-emotion
features in sentence emotion prediction, and suggest that the adverb-emotion rules
could provide a more reliable feature.
To the best of our knowledge, this is the first work of exploring the function of
Japanese adverbs in emotion expressions. In our future study, we plan to extend this
work to multi-languages, to find the specialities of adverbs for emotion expression in
Developing a Japanese Adverb-Emotion Corpus
111
different languages. We also plan to combine more subtle textual features with these
adverb-emotion features in the classifier, to further improve the sentence emotion
classification results.
Acknowledgments
This research has been partially supported by the Ministry of Education, Science,
Sports and Culture of Japan, Grant-in-Aid for Scientific Research(A), 15H01712,
by National Natural Science Foundation of China under Grant No. 61432004, the
National High-Tech Research & Development Program of China 863 Program under Grant No. 2012AA011103, the Scientific Research Foundation for the Returned
Overseas Chinese Scholars, State Education Ministry, and Key Science and Technology Program of Anhui Province, under Grant No. 1206c0805039, and the China
Postdoctoral Science Foundation funded project, under Grant No. 2014M560351.
Appendix A. Appendix
We demonstrate the adverb-emotion rules in Table 13. In this detailed demonstration, S(Int(e1 , Int(e2 ), . . . , Int(e8 ))) represents the emotion intensity of a sentence S
while adv(Int(e1 , Int(e2 ), . . . , Int(e8 ))) represents the emotion intensity of an adverb
adv. The functions of adverbs in the increment and decrement of emotion intensities
as well as in the change of sentence emotion labels are also annotated.
Type
Condition
りんりん
rinrin
0
no condition
とても
very
1
no condition
すこし
a little
2
no condition
にこにこ
smiling
3
no condition
わかれ
わかれに
Become
apart of
4
no condition
どうにか
somehow
5
no condition
Example
暑い夏に、りんりんという風鈴の音を聞
いたら、少し涼しく感じるだろう。
In hot summer, when you hear the
sound of wind chimes that rinrin, you
will feel a little cool.
昨日読んだ小説はとても面白かった。
The novel that I read yesterday is very
interesting.
先日の風邪はすこしよくなった。
The recent cold has become a little better.
彼女は先生に褒められて、にこにこと笑っ
た。
She has been praised by teachers, smiling and laughing.
戦争でわかれわかれになった家族の悲劇
が、今の続いている。
The tragedy that the family became
apart in the war is still ongoing.
成績はあまりよくないが、どうにか合格
だけはできた。
Performance is not very good, just
somehow was able to pass.
Sentence
Emotion
Adverb
Emotion
Intensity
Change
Int
(Love)
=0.7
Int
(Love)
=0.9
Int
(Joy)
=0.5
0.3
-0.2
Int
(Joy)
=0.9
Int
(Joy)
=0.6
Int
(Sor)
=0.8
Int
(Sor)
=0.5
Int
(Joy)
=0.9
Int
(Anxiety)
=0.2
Emotion
Change
Y. Sun, F. Ren, C. Quan
Adverb
112
Table 13: Structure of Japanese adverb-emotion corpus.
せっせと
diligently
さも
seem
あっさり
plain,
easily
6
adv+verb
山本さんあ週末も休まないで、せっせと
仕事をしている。
Mr. Yamamoto do not rest on weekends, has been working diligently.
7
adv+adj;
adv+adj+
そうに+verb
/そうな+n
山田君はさもおいしそうに食べている。
It seems that Mr. Yamada is eating
with flavour.
3
ねちねち
saying
that
sticky
3
3
Int
(Hate)
=0.6
Int
(Love)
=0.7
Int
(Love)
=0.6
Int
(Love)
=0.5
Int
(Joy)
=0.8
0.2
Int
(Hate)
=0.8
Int
(Hate)
=0.3
Int
(Anxiety)
=0.9
Int
(Anxiety)
=0.3
Developing a Japanese Adverb-Emotion Corpus
1
あの男の人はあっさりした性格でいいで
すね。
adv+する
The man is plain and has good personality.
この難しい問題をいともあっさりやって
adv(と)+verb のけた。
This difficult problem was solved easily.
sent contains
くっつ く/粘
彼は他人の悪口ばかり、ねちねち言って
る/
いる。
つ く/張 り 付
He just speak ill of others, saying that
く/
sticky.)
つける/貼る/
貼り付ける
ガス管が古くって、ねちねち粘りだした
sent contains のを使っている。このままで置いたらガ
文句/言う/
ス中毒だった。
絡みつく
The old gas tube is sticky. It will get
poison if let it be.
Int
(Love)
=0.6
113
adv+する
0
sent contains
木/葉/雨/
涙/
雪/花/散る/
当 た る/落 ち
る
Int
(Anxiety)
=0.8
秋になって木の葉がはらはら散るのを見
ていると、さびしい気持ちがする。
In the fall, leaves on the trees are falling
one after another, the kind of lonely
come to feel.
Int
(Sorrow)
=0.6
Int
(Anxiety)
=0.7
Y. Sun, F. Ren, C. Quan
3
114
はらはら
heart going
pit-a-pat,
falling
rapidly in
big drops
大通りで遊んでいる子供を見るとはらは
らする。
It was a white-knuckle when looking at
the children playing in the avenue.
Developing a Japanese Adverb-Emotion Corpus
115
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Yan Sun
received her M.E. degree from Dalian University in
2008, her B.E. degree from Dalian University of Foreign Languages in 2004. She is currently a Ph.D student in Tokushima University. Her research interests include Natural Language Processing and Affective Computing. Faculty of Engineering, University of Tokushima,
2-1, Minamijyousanjima-cho, Tokushima 770-8506 Japan
116
Y. Sun, F. Ren, C. Quan
Fuji Ren
(Member)
received his B.E. and M.E. degrees from Beijing University of Posts and Telecommunications, Beijing, China, in
1982 and 1985, respectively. He received his Ph.D. degree
in 1991 from Hokkaido University, Japan. From 1991, he
worked at CSK, Japan, where he was a chief researcher of
NLP. From 1994 to 2000, he was an associate professor in
the Faculty of Information Sciences, Hiroshima City University. He became a professor in the Faculty of Engineering of the University of Tokushima in 2001. His research
interests include natural language processing, artificial intelligence, language understanding and communication,
and affective computing. He is a member of IEICE, CAAI,
IEEJ, IPSJ, JSAI, and AAMT, and a senior member of
IEEE. He is a fellow of the Japan Federation of Engineering Societies. He is the president of the International
Advanced Information Institute. Faculty of Engineering,
University of Tokushima, 2-1, Minamijyousanjima-cho,
Tokushima 770-8506 Japan
Xin Kang
received his Ph.D degree from The University of
Tokushima, Tokushima, Japan, in 2013, his M.E. degree from Beijing University of Posts and Telecommunications, Beijing, China, in 2009, and his B.E. degree
from Northeastern University, Shenyang, China, in 2006.
He is currently a postdoctoral fellow in Tongji University,
and a foreign researcher at the University of Tokushima.
His research interests include statistical machine learning,
graphical models, and text emotion prediction. School of
Electronics and Information, Tongji University, 1239 Siping Road, Shanghai, P.R. China
Changqin Quan
received the Ph.D. degree in 2011 from Faculty of Engineering, University of Tokushima, Japan. She is currently
an associate professor in Kobe university. Her research interests include Natural Language Processing, Affective
Computing, and Artifficial Intelligence. Graduate School
of System Informatics, Kobe University, 1-1 Rokkodai,
Nada, Kobe 657-8501, Japan