AFFECTIVE BLOG ANALYZER

What People Feel to

Masato Tokuhisa, Jin’ichi Murakami and Satrou Ikehara

Department of Information and Electronics, Graduate School of Engineering

Tottori University, Koyama-Minami, Tottori, Japan

Keywords:

Affect, Emotion, Sentence pattern, Sentiment analysis, Web mining.

Abstract:

This paper proposes an affective blog analyzer which can capture people’s emotional targets. The existing

affective analysis has some problems. For instance, polarity analysis or positive/negative classiﬁcation for

documents are developed, but emotional targets can not be extracted. Some investigations can capture cus-

tomer’s wanted/needed objects, but the knowledge is domain dependent. Therefore, it can not analyze people’s

everyday life. Against these problems, this paper uses a sentence pattern dictionary to analyze emotions. The

dictionary covers Japanese fundamental 6,000 verbs and contains 14,800 patterns with emotional information

for everyday life. This dictionary is available for analyzing the downloaded blog articles. After analyzing

blogs, many keywords can be extracted as emotional targets. In order to ﬁlter and sort them for supporting

blog analysts, two parameters are applied. One is Z-score in terms of the frequency of the target appearance,

and another is probability of emotions. In the experiments, trendy and emotional targets were successfully

extracted from 6-month-blogs. Thus, the effects of the patterns and parameters are conﬁrmed.

1 INTRODUCTION

WWW is used by many people from children to elder

persons. Partially, blogs are popular among people to

describe their everyday life. Such documents contain

people’s favorites, interests, desires and hates. There-

fore, blogs are good resources for marketing analysts,

political decision makers, and so on.

In the previous studies on sentiment analysis and

web mining, a lot of ideas are proposed and realized.

On early studies of sentiment analysis, the positive

and negative semantic orientation/polarity of the con-

joined adjectives were focused on (Hatzivassiloglou

and McKeown, 1997). As the polarity of the adjec-

tives is a strong clues, the techniques for document

classiﬁcation of reviews were developed by using ma-

chine learning (Turney, 2002). Recently, the attention

of the studies shifts and more functions are required.

Some investigations realize to extract the targets of

the polarity, for instance, customers’ needs/wants

(Kanayama and Nasukawa, 2008). In order to extract

the targets, pattern based method is effective. But the

patterns are developed with focusing on several spe-

ciﬁc domains like book-reviews, computer-products,

and so on. The knowledge base engineers avoided

the development of the knowledge base for everyday

life. One reason is that they believe such knowledge

base is futile for business on the engineering stand-

point. But recently web mining techniques are re-

quired from advertisement agents. Another reason is

that such knowledge base requires complex expres-

sions or meaning. For example, OCC model on the

cognitive science represents the structure of emotion

causality (Ortony et al., 1988). If text parser can ana-

lyze such structure from text, emotion reasoning will

succeed. A frame knowledge base was developed

and it worked on the virtual agents very well (Elliott,

1992). However, there is no pattern knowledge base

to parse and analyze natural language expressions.

There is a big gap between the frame knowledge and

natural language expressions, as semantic analysis is

not accomplished. Some investigations tried to ob-

tain such knowledge from Web or huge corpus with

focusing on the relation, SUBJECT-VERB-OBJECT-

OBJECT (Liu et al., 2003), (Tokuhisa et al., 2008).

This is the clue of the verbs. It is as important as

the clue of the adjectives. But they also have not

extracted the emotional targets yet. Since their ap-

proach depends on the OBJECT, if unknown keyword

is given to the OBJECT, there is no proof to analyze

emotions successfully. Against the problems, for in-

stance, emotion reasoning for everyday life and ex-

247

Tokuhisa M., Murakami J. and Ikehara S. (2010).

AFFECTIVE BLOG ANALYZER - What People Feel to.

In Proceedings of the 2nd International Conference on Agents and Artiﬁcial Intelligence - Artiﬁcial Intelligence, pages 247-252

DOI: 10.5220/0002719602470252

 SciTePress

traction of emotional targets, this paper uses a sen-

tence pattern dictionary. The dictionary originates

from A-Japanese-Lexicon which covers Japanese ba-

sic 6,000 verbs and contains 14,800 sentence patterns

(Ikehara et al., 1997). Affective information is added

to the dictionary in order to infer emotions for every-

day experiences.

Next, for the purpose of supporting blog analysts,

this paper develop a blog analyzer based on the dictio-

nary. The blog analyzer contains both the blog crawl-

ing function and the emotion reasoning function. The

emotion reasoning outputs not only emotional cate-

gories but also emotional targets. The keyword of the

emotional target would be a great help to analyze the

interests/claims among people. In the experiment of

this paper, the emotional target extraction is demon-

strated.

2 EMOTIONAL PATTERN

DICTIONARY

2.1 How to Analyze Emotions from a

Sentence

Our basic idea to analyze emotions from a sentence

is the emotional feature extraction from text, in other

words, the conﬁrmation of the emotional process. If

an input sentence represents a part of emotional pro-

cess, emotions are inferred form the sentence. The

emotional process consists of emotion arousal, emo-

tional state and emotional response. It is not easy to

deﬁne emotional state with separating from emotional

arousal/response on the cognitive science viewpoint,

but it is clear that emotional states are described in

natural language.

These are examples of the three:

Arousal: I left my wallet at the airport.

State: I disappointed myself.

Response: My tears fell down.

In the arousal process, there are some features, in

other words, “emotional cause.” The causality is

explained by OCC model. But it was too abstract

to analyze natural language expressions. Therefore,

more detailed features are referred from (Tokuhisa

and Okada, 1997), which contains 36 features for

joy/sadness and 120 features for eight emotions in to-

tal. For example, “loss” is one of the features of sad-

ness. In contrast, “acquisition” is one of the features

of joy.

2.2 Constructing Emotional Pattern

Dictionary

There already exists a pattern dictionary (Ikehara

et al., 1997), which is developed for machine transla-

tion from Japanese to English. It covers fundamental

Japanese verbs and can distinguish word sense ambi-

guity by the valency grammar which is a kind of con-

straint for dependency among subject case, verb and

object case in a sentence.

Following is example

ex1) N1(person) lose N2(concrete object)

= feature “loss”

ex2) N1(person) lose N2(disease)

= feature “inner-pleasure”

Here, N1 and N2 are variables which match a

noun word or phrase. Since the meaning of the sen-

tence depends on the meaning of the variables, the

variables are restricted what expression matches.

In order to extend the dictionary for emotion rea-

soning, the meaning of the pattern was checked, and

emotional information is assigned. The informa-

tion slots are “emotional cause,” “emotion category,”

“feeler,” and “feel-to.” Figure 1 shows real samples

of the dictionary.

The emotion category consists of gladness, sad-

ness, liking, dislike, surprise, expectancy (hope), fear,

anger and non-emotion. These are named 9-category-

set in this paper.

Since tense and aspect are ignored to analyze emo-

tions in this paper, 9-category-set can not be dealt

with well. Therefore, 5-category-set and 3-category-

set are prepared. 5-category-set consists of P, N, A, S

and non-emotion.

P is union of gladness, liking and expectancy.

N is union of sadness and fear.

A is union of disliking and anger.

S is surprise.

3-category-set consists of positive, negative and non-

emotion.

Positive is P.

Negative is union of N and A.

Non-emotion of 3-category-set includes S.

This construction was hand made. It took 3 years

to add emotional information. The current version is

the 3rd. edition. The 2nd. edition was checked by 3

analysts. As the result, the error ratio was 14.2%. The

3rd. edition will be better.

While this dictionary is constructed for Japanese, these

examples are written in English in order to explain the sig-

niﬁcant concept of the dictionary.

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248

Japanese pattern: N1(agent)ga N2(*)wo

N3(agt.)ni N4(numeric)de

kau

English pattern: N1 buy N2 for N3 for N4

emotional cause: feature “acquisition”

emotion category: gladness

feeler: N1

feel-to: N2

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

emotional cause: feature “acquisition”

/ “hospitality”

emotion category: gladness / gladness

feeler: N3

feel-to: N2 / N1

Japanese pattern: N1(agt.)ga N2(obj.,abst.)wo

N3(agent,place)kara

ryakudatsu suru

English pattern: N1 plunder N2 of N3

emotional cause: feature “loss” / “cheating”

emotion category: sadness / anger

feeler: N3

feel-to: N2 / N1

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

emotional cause: feature “acquisition”

emotion category: gladness

feeler: N1

feel-to: N2

* The italic words are Japanese.

Figure 1: Samples of sentence patterns and their emotional

information.

3 AFFECTIVE BLOG ANALYZER

3.1 Components

Affective blog analyzer (ABLANA) is newly de-

veloped. Figure 2 shows the basic components of

ABLANA. This system monitors some blog sites by

referring the RSS of the blog sites, downloads new

articles from them, and then performs emotion rea-

soning by using the pattern dictionary. The results are

statistically analyzed and summarized.

3.2 Behavior

ABLANA watches RSS derived from blog sites to ex-

tract new articles with a time series. Therefore web

search engines are not used.

“Downloader” accesses to the blog site and down-

load the article informed by the RSS after waiting for

24 hours more. If the article is temporary or SPAM,

it would be removed. If the article is written well, it

RSS Watcher

URL List

Downloader

Blog Articles(HTML)

Text Extractor

Emotion Categories & Emotional Targets

Statistical Analyzer

The Internet

Blog Sites

Pattern

Dictionary

Blog TextsEmotion Reasoner

Emotional Targets

with Trend & Affective Parameters

output

input

ABLANA

Site

List

Figure 2: Components of ABLANA.

would receive some comments from the readers. In

this paper, the comments are not referred yet, but it

would be useful information to measure the reliabil-

ity of the articles.

“Text extractor” parses HTML sources and ex-

tracts blog body, author’s name, date, title of the ar-

ticle and so on. Because some people do not write

comma and period onto the text in Japanese blogs, the

extractor ﬁnds sentence terminal and splits sentences.

“Emotion reasoner” performs morphologic analy-

sis and pattern matching to the blog body texts. Then,

it selects the best pattern-matching according to the

constraint to the variables in patterns. As the results,

emotional information is obtained.

“Statistical analyzer” collects emotional targets

from the results and then assigns score to each of tar-

get for ﬁltering and sorting the targets according to

some parameters.

3.3 Parameters for Emotional Targets

The requirements to the statistical analyzer are fol-

lowing:

(a) to extract something trend among people, and

(b) to evaluate their polarity.

The requirement (a) is measured by the burst of

keyword. In this paper, Z-score z

k,i

is used to capture

it. Z-score is described as the following equation.

k,i

= (x

k,i

− m

)/σ

(1)

AFFECTIVE BLOG ANALYZER - What People Feel to

249

∑

j∈I

k, j

(2)

∑

j∈I

k, j

− m

)

(3)

I is a set of intervals (In this paper, one interval is

one week, and whole intervals are about six months).

k is the keyword to be scored. i is one of the intervals

to be analyzed. x

k,i

is the frequency of the appearance

of the keyword k as the emotional target during the

interval i. m

is the mean of the x

k, j

, here j is each

element of I. N

is the number of elements of I. σ

the standard deviation.

The requirement (b) is calculated by the probabil-

ity P(e|k, i) of the appearance of emotional category e

after the keyword k appeared on the focusing interval

i. The equation for it is as follows:

P(e|k,i) = x

k,i,e

k,i

(4)

4 EXPERIMENTS OF

EMOTIONAL TARGET

EXTRACTION FROM BLOGS

4.1 Terms and Amount

ABLANA ran from August 1st 2008 to January

31st 2009. From three Japanese major blog

sites, 7,120,992 articles(105,167,276 sentences) were

downloaded. The emotion reasoner spent about 10

days to process one-month-articles. The ﬁle size is

53 GB a month, which includes blog texts, pattern-

matching results, and emotional targets.

4.2 Experiment-1: Basic Performance

The basic performance of the emotion reasoning is

evaluated by the accuracy A. It is calculated by A =

2N(o ∩ c)/(N(o) + N(c)). Some analysts annotate

emotional tags to test sentences. The results of emo-

tion reasoning are compared with the tags. N(c) is the

number of tags by the analysts. N(o) is the number of

tags by ABLANA. N(o∩ c) is the number of tags cor-

responding between the analyst and ABLANA.

304 sentences are extracted from blog texts by

random. 5 persons annotated 9-category emotional

tags to these sentences. Table 1 shows the accuracy

of the emotion reasoning. The column of HUMAN

shows the difﬁculty of the emotion reasoning. On 9-

category-set, the accuracy of ABLANA is lower than

that of HUMAN. But on 5- and 3-category-sets, the

Table 1: Accuracy of emotion reasoning.

E-Categories ABLANA HUMAN

9 0.375 0.513

5 0.592 0.566

3 0.685 0.618

ABLANA’s accuracy is closed to HUMAN’s. There-

fore, in this paper, 3-category-set is used to the prob-

ability scoring.

4.3 Experiment-2: Observation with

Keyword

The aim of this experiment is to conﬁrm the abil-

ity of Z-score and emotion probability. Before us-

ing Z-score to select trend keyword as the main pur-

pose, in this section a prominent keyword is given to

ABLANA and the score is observed along a time se-

ries.

This experiment is a top-down observation ap-

proach. For instance, we give a known keyword and

check whether a known event can be captured or not.

There was a lot of big change in 2008. In the do-

main of the motor sports, “HONDA Racing F1 Team”

exited Formula One on December 5th.

A keyword “HONDA” repeatedly appeared whole

intervals, and the burst appeared twice as shown

in Figure 3. This keyword appeared approximately

19,000 times and 4.68% of the appearance was emo-

tional target. The ﬁrst burst corresponds to the exit-

ing news. Some news articles about the exiting were

cited on blogs and blog authors wrote comments.

The second burst corresponds to another exiting news

– “HONDA exited eight hours endurance motorbike

race on January 23rd 2009.”

Z-score becomes high at the burst. Therefore Z-

score has feasibility to ﬁnd a signiﬁcant event.

The right Y axis of Figure 3 shows the probabil-

ity of P(POSITIVE|“HONDA”,i) along a time series.

The exiting news were not good for motor sports fun,

but the P

positive

was over 50%. Reading the blogs, it

was clariﬁed that blog authors expressed their love to

HONDA.

On the other hand, P

positive

became minimum on

November21st 2008 and January 9th 2009. The blogs

on these days said as following:

• a negotiation episode at a HONDA dealer includ-

ing customer’s distrust

• a decrease in production of a HONDA factory

• early history of HONDA including an expose

As the results, the probability leads to appropriate

articles for a blog analyst successfully.

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500

1000

1500

2000

2500

3000

3500

4000

Feb/01/2009Dec/01/2008Oct/01/2008Aug/01/2008

100

Frequency

Probability[%]

Term

Freq. as Emotional Target

Freq. at Any Places

Prob. of Positive Emotion

X axis is term (from Aug. 1st 2008 to Jan. 31st 2009).

The circlet-plots are the appearance as an emotional target,

and the rectangle-plots are the appearance at any places in

the blogs(left Y axis). The X-plots are the probability of

positive emotion to the target(right Y axis).

Figure 3: Frequency of appearance “HONDA” and Proba-

bility of its positive emotion.

Table 2: High positive targets.

# Z P

pos.

Emotional target

1 2.7 98.6 a good ﬁght

2 2.0 98.2 kart

3 4.8 97.8 Olympic opening ceremony

4 4.8 97.6 large fonts

5 4.8 97.1 medal competitions

6 3.1 97.1 corridor

7 4.8 96.9 the anchor of the Olympic

sacred-ﬁre relay

8 4.8 96.9 Industrial Hi-School of Naruto

9 3.9 96.8 resting

10 4.8 96.8 Secretary Romaiya

4.4 Experiment-3: Observation with

Z-score and Probability

Emotional targets can be ﬁltered by the Z-score and

listed in the order of emotional probability each in-

terval. For example, Table 2 and 3 show emotional

targets whose Z-score is greater than 2.0 from Aug

8th. 2008 to 14th. These keywords are intuitively

correct. People seem to have enjoyed Olympic and

disappointed at the loss of the games. By the way,

there are high schools in the lists. Because there is an

annual baseball tournament for high school students

in Japan, they are some of the teams.

On the other hand, emotional targets are ﬁltered

by the lower Z-score condition (0.5 < z < 2.0). Ta-

ble 4 shows the targets. Some targets are useful and

the probability looks appropriate. We can see sev-

eral categories from the targets. For instance, sports,

Table 3: Low positive targets.

# Z P

pos.

Emotional target

1 4.8 2.2 weapon

2 4.7 2.5 Kagoshima Jitsugyou Hi-School

3 4.6 3.2 soaring prices

4 4.4 3.9 professionalism

5 4.2 4.4 Olympic baseball; Japan, Cuba

6 3.3 5.4 ten

7 2.5 5.6 ruddy

8 4.8 5.6 Olympic soccer; Japan, Nigeria

9 4.5 6.0 job

10 2.2 6.3 discussion

amusements, communication and health are found in

the high positive groups, and disease and politics are

found in the low positive groups.

“Aso” is a politician in Japan. His name appeared

in both the group of 80% – 90% and the group of 0%

– 10%. We can understand that the opinion for him is

split. Of course, we must read and analyze the blogs

to conclude this idea. But we can say that the table

leads us what blogs to be read. This process is a good

example for how to use ABLANA.

As the results, the combination of Z-score and

emotional probability would be useful parameters to

ﬁnd people’s requirements and complaints.

5 OPEN PROBLEMS

The soundness of the patterns and parameters pro-

posed in this paper is conﬁrmed well by the experi-

ments. However there remain some problems to im-

prove the accuracy of emotion reasoning.

One of the important requirements is to deal with

the plural sentences. For example, “Beaujolais Nou-

veau’s Release. It tastes different every year. This

year’s wine is fresh and ﬂavors...” has three sen-

tences. The second and third sentences do not include

“Beaujolais Nouveau”. The proposed method can not

extract “Beaujolais Nouveau” as the emotional target

from the two.

Moreover, because “release,” “fresh” and “ﬂa-

vors” are positive words and “tastes different” is a

negative phrase, the probability of emotion for this ar-

ticle is marked a little negative. The second sentence

describe just a common sense, but it is not complain.

Therefore, our future works are to analyze

anaphora and to merge the emotional states from plu-

ral sentences.

AFFECTIVE BLOG ANALYZER - What People Feel to

251

Table 4: Emotional targets found in the middle Z-score set.

Range of P

pos.

/ Sample of emotional targets

90% – 100%/ company, foreign exchange, accident,

lunch, newest information, image, cofﬁn, knowledge,

going to sleep, customer, cover, lottery, Yakisoba(fried

noodles), new mail, salary

80% – 90%/ rest, musical, garlic, house, animation,

bumber ticket, ambulance car, dam, soft cream, ticket,

actual place, plan, Mr. Aso, Soumen(ﬁne noodles), 3 days,

revision, high school basebale tournament

70% – 80%/ chance, Gyoza(pot sticker), Obon

(Japanese Summer holidays), clothes, reference book,

sheep, access counts, proposal, boyfriend/girlfriend, son,

once, Japanese people, stairs, guys, past questions, movies

60% – 70%/ cloud, characteristics, senior, process,

short, street stall, milk, potato, world view, ﬂower langu-

age, high school days, panel, course, room, Mr. Kouichi,

response, joy, park, Tokyo, tombstone, TVCM, RPG

50% – 60%/ helmet, boys, Japanese, lecture, environ-

ment problem, curren, strain (feeling), water place, safe

management, game, Yankees, vanilla, category, climbing

Mt. Fuji, Beawanpi(one-piece dress), expected software,

40% – 50%/ fear, triathlon, batted ball, immediately

after, sitting comfort, Osaka Touin(school), young people,

ear, mood, Shun(person), dark, whole life, love, future,

meal time, driving, competition, ultraviolet rays

30% – 40%/ obligation, corner, leukemia, prejudice,

under construction, terrorist, feeling of intimacy, Hikari

(light, ﬁber-optic cable), natural environment, belly,

member on the regular payroll, symptoms, oneself

20% – 30%/ abnormal weather, contents of work,

darkness, otherwise, fatigue, narrow, crowdedness, life, SAP

for maker, love, elegant, property management, shooting

(ﬁlm), a liitle happy, aphthous ulcer, drawback, husband

10% – 20%/ HIV, bewilderment, muddy, allow, noon,

twice or more, provery of blood, marriage, put away, 30,

your body, a hip joint, load (of baggage), shame, headache,

next election of Presentative, scarcity of water, hallucination

0% – 10%/ Furafura(dizzily), Japan High School

Baseball Federation, molar tooth, panic, a chief secretary-

Aso Taro, continuation, breast cancer, camp, sleeplessness,

respect, strained back, ant, sleeping posture

* Extracted from the interval Aug. 1st - 7th 2008. Trans-

lated into English by the author of this paper.

6 CONCLUSIONS

This paper proposed an affective blog analyzer

(ABLANA) which crawls blog articles along a time

series from the Web and analyzes people’s emotional

targets. The method for emotion reasoning uses a sen-

tence pattern dictionary. The original dictionary is

A-Japanese-Lexicon. It covers Japanese fundamental

6,000 verbs and consists of 14,800 patterns. In this

paper, the extended dictionary is used, where emo-

tional information is annotated if pattern expresses

emotional processes (arousal, state and response).

In the experiments, the extracted emotional tar-

gets can be ﬁltered and sorted by two parameters,

for instance, the Z-score in terms of the frequency of

the keyword appearance and the probability of emo-

tions. These parameters are so effective that trendy

and emotional targets can be captured. Thus, the do-

main independent affective analyzer is successfully

constructed. It is expected to practically apply this

technique to capture people’s affective statements.

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