Interactive Aspect Summarization Using
Word-Aspect Relations for Review Documents
Kazutaka Shimada, Masashi Yamaumi, Ryosuke Tadano, Masashi Hadano and Tsutomu Endo
Department of Artificial Intelligence, Kyushu Institute of Technology
680-4 Iizuka Fukuoka 820-8502 Japan
shimada, m yamaumi, r tadano, m hadano, endo@pluto.ai.kyutech.ac.jp

Abstract—As the World Wide Web rapidly grows, a huge
number of online documents are easily accessible on the Web.
We obtain a huge number of review documents that includeuser’s opinions for products. To summarize the opinions is oneof the hottest topics in natural language processing. We focus
on aspects of a product in the summarization process. First,
we identify a relation between aspects and each word in review
documents. Our method employs an unsupervised approach forthe identification process. The experimental result shows theeffectiveness of the method. Next, we generate a summary byusing the relations. In our system, users obtain the summary byan interactive approach.

I. I NTRODUCTION
As the World Wide Web rapidly grows, a huge number of
online documents are easily accessible on the Web. Finding
information relevant to user needs has become increasingly
important. The most important information on the Web isusually contained in the text. We obtain a huge number ofreview documents that include user’s opinions of products.
When buying a product, users usually survey reviews of the
product. More precise and effective methods for evaluating
the products are useful for users. To analyze the opinions is
one of the hottest topics in natural language processing [1],[9], [12], [18].
In this paper we focus on a summarization task for review
documents of a product. In review summarization tasks,systems often treat aspects of the target products. Lu andZhai [9] have proposed an opinion integration method withaspects using the PLSA model. The purpose of their study
is to add supplementary opinions and similar opinions into
reviews. Blair-Goldensohn et al [1] have proposed a systemthat summarizes the sentiment of reviews for a local service
such as a restaurant or hotel. They classified the polarity
of sentences by using the maximum entropy method with
WordNet and ratings in reviews. However, machine learning
methods usually need a large amount of training data to
generate a high accuracy classifier. They also reported two
aspect extractors; dynamic aspect extraction and static aspect
extraction They combined them and summarized review texts.
However, sentences with high polarity scores are not always
suitable for summarized texts because of the variety of users’
needs.
In this paper we present an aspect summarization system
for review documents. First, we identify aspects of eachword in the target review documents. In this process, weemploy two characteristics in the review documents; structure
information and rating information. Next, we identify the
aspect of a word by using features from structure information
and rating information. Finally we summarize the review
documents on the basis of the word-aspect relations. We
apply an interactive approach to the summarization process
for the variety of users’ needs. In the process, the system
displays words with high aspect scores from the word-aspect
identification process first. Then it extracts output sentencesfrom the review documents by using the words selected byusers.
In Section 2, we explain the aspect identification process
for the summarization. In Section 3, we describe the summarization process using word-aspect relations acquired in theprevious section and conclude this paper in Section 4.
II. W ORD -A SPECT I DENTIFICATION
For summarization, we need to identify relations between
words and aspects in review documents. Figure 1 shows theoutline of the word-aspect relation identification process. In
this section, we explain a method to extract features for theidentification process first. Next, we propose an identification
method based on a scoring approach with the extracted
features. Finally, we evaluate the identification method with adataset.
A. Feature extraction
We focus on two characteristics in the feature extraction;
structure information and rating information in review documents. In this process, we handle nouns, adjectives verbs 1 andadverbs. We use the morphological analyzer ChaSen [11].
1) Structure information:
Structure information in review documents is useful for
sentiment analysis. Kaji and Kitsuregawa [6] have proposeda method for building sentiment lexicon by using layout
structures; itemization structure and table structure.
In our dataset, users often itemize opinions in the reviewsof a product 2 . In multi aspect review documents, theitemization is usually based on each aspect in the review
form. We focus this structural information (SI) for the feature
1 We use a stopword list for verbs. The stopword list includes highfrequency verbs such as “is” and ‘think”.
2 As a preliminary experiment, we examined 670 review documents in adataset. The result said that 130 documents in them contained itemization.

Review documents

Target words; Nouns
Word-aspect relation identification
based onScoring with SI and RIFeature extraction
Using Structure Information (SI)Using Rating Information (RI)

Fig. 1.

The outline of word-aspect identification.

Product name: NotePC001
Ratings: Performance: 4

Price: 3

Portability:1

Review:
Itemized area

* Performance

Aspect: PerformanceThis PC has a very fast processor!

Related words: PC, very fast, processor

Also it has a huge 256GB SSD hard drive!

figure, the words such as “heavy” and “light” are associatedwith the aspect “Portability”. On the other hand, the word
“weight” is deleted from the characteristic word list because
the word appears in many reviews evenly.

huge, SSD, hard drive* Price
The price is average. ....

B. Scoring(a)

Product name: NotePC001
Ratings: Portability:1

Product name: NotePC002
Ratings: Portability: 5

Product name: NotePC003
Ratings: Portability: 3.......
But the weight is very heavy.

.......
The weight is extremely light!

.......
The weight is so-so.

.......

.......

.......

* Feature words
The words in reviews with high or low ratings: very, extremely, heavy, light.
* Non-fuature words
The words in all reviews or normal ratings: weight, so-so.

(b)Fig. 2.

The characteristics for the feature extraction.

We identify the aspect of a word by using SI and RI
features. First, we compute the scores of words of SI and
RI features for each aspect.

    



(1)

where  and  denote the number of review documents andthe number of documents containing word  , respectively.  and are the frequency of  and the number of words in thesentences about a feature set (SI or RI) and an aspect ( ),respectively. We normalize the scores to [0,1]. The score of aword  is computed as follows: 
extraction. For the process, we use template patterns such as
Line head mark + Aspect name +Separator or New line + Sentence area
We regard words in the “Sentence area” as related words forthe “Aspect name”. Figure 2 (a) shows an example of thecharacteristic. In the figure, “*” is the line head marker. Our
system detects “sentence area” on the basis of the next line
head marker. Then, it extracts characteristic words such as
nouns from the sentence area. We regard the extracted words
as the related words of the aspect “Performance”.
2) Rating information:
Another characteristic is based on rating information (RI) in
review documents. Rating information is one of the mostimportant features for sentiment analysis [14] and sentimentsummarization [10], [17]. Reviews with a high or low rating
for an aspect contain sentences related to the aspect. The
target is a review document of which an aspect is a high or
low value and other aspects are normal values. Figure 2 (b)shows an example of the characteristic. In the figure, the threereviews contain 1, 3 and 5 points for the aspect “Portability”,
respectively. Here we assume that the range of the rating is 1-5
points. Our system extracts words that are included in reviews
with high (5 points) or low (1 point) ratings and ignores words
that are in reviews with average ratings (3 or 4 points). In the      

(2)

Next, we identify the aspect of a target word by using
the scores. In this process, we use four words in front andbackward of the target word in the sentence as features forthe identification. The score of a word   belonging to an
aspect  is computed as follows:

 ¾

       

(3)

where  is features for the target word   .   is thefrequency of .  
is a weight value of the partof-speech tag of the . In this paper, the weights of nouns,
adjectives verbs and adverbs are 0.5, 1.0, 0.1 and 0.3 respecdoes
tively. These values are determined experimentally. If
not exist in the feature set explained in Section II-A, the value
is 0. We select the aspect that contains the maximum scoreas the aspect of the word. Figure 3 shows an example ofthe scoring process. First our system detects sentences whichcontain the target word “image”. Next, it extracts words whichsurround the target word. Here we assume that there are three
aspects and a word list with scores which are computed by Eq(1) and Eq (2). Our system computes   of each aspect
by using Eq (3). In this example the aspect of the target word
“image” is the aspect  .

Target word: image

The Word-Aspect Relation List:

Sentences:
The quality of the image is high.
The high level images!
Features:quality: 1, level: 1, high: 2, image: 2* image
Originality: 0.3
Graphics: 0.9
Music: 0.05.....

* sound
Originality: 0.4
Graphics: 0.01
Music: 0.8.....

* quality
Originality: 0.3
Graphics: 0.5
Music: 0.5.....

Feature set of each aspect from Eq (1) and (2)Aspect x
quality: 0.2
level: 0.2
high: 0.3
image: 0.4

Aspect yquality: 0.4
level: 0.5
high: 0.4
image: 0.9

Aspect z
quality: 0.2
level: 0.4
high: 0.3
image: 0.6

Aspext x : 1*0.2*0.5+1*0.2*0.5+2*0.3*1.0+2*0.4*0.5= 1.2
Aspext y : 1*0.4*0.5+1*0.5*0.5+2*0.4*1.0+2*0.9*0.5= 2.2
Aspext z : 1*0.2*0.5+1*0.4*0.5+2*0.3*1.0+2*0.6*0.5= 1.5

Target word: image >> Aspect y

Fig. 3.

Opinions from a user

An example of a review document.

C. Experiment
In this paper we handle review documents about gamesoftwares. We extracted 4174 review documents from a Web
site3 . We constructed a feature set described in Section II-A
from them. Figure 4 shows an example of a review document.
The review documents consist of evaluation criteria, theirratings, positive opinions, negative opinions and comments
for a product. The number of evaluation criteria is 7: “Originality (O)”, “Graphics (G)”, “Music (M)”, “Addiction (A)”,“Satisfaction (S)”, “Comfort (C)”, and “Difficulty (D)”. In
this paper, we regard the evaluation criteria as aspects of thereview documents.
First, we evaluated our method with 130 review documents
3 http://ndsmk2.net* Originality: image (0.3) + quality (0.3) = 0.6* Graphics:
image (0.9) + quality (0.5) = 1.4* Music:
image (0.05)+ quality (0.5) = 0.55

identify - >> Aspect with Max Score: Graphics
The quality of the sound is good.
* Originality: quality (0.3) + sound (0.4) = 0.7* Graphics:
quality (0.5) + sound (0.01)= 0.51
* Music:
quality (0.5) + sound (0.8) = 1.3

identify - >> Aspect with Max Score: Music

Fig. 5.

Examples of the sentence classification task.

An example of the scoring process.

Evaluation criteria and their values:
Originality: 2 pts, Graphics: 4 pts, ...

Fig. 4.

Target Sentences:
The fantastic image quality!!

about a game software. The number of words in the documents
was 1817. We selected 20 words for each aspect, namely
140 words, from them as the test dataset. Table I showsthe experimental result. “Words” in the table denotes thenumber of words that were classified into each aspect. The
average of the recall rate was 72.1%. The precision rates of“Originality” and “Addiction” were lower than the others. The
reason why the precision rates were low was that “Originality”
and “Addiction” are generally related to “Satisfaction”. In
this experiment, words about “Satisfaction” were often incorrectly classified into “Originality” . Tadano et al. [15] havereported that even human beings tend to confound words about“Originality” with words about “Satisfaction” in a sentiment
annotation task.
The tabel I was the result of an evaluation with limited
words. Next, we evaluated the word-aspect relations identified
by our method on overall viewpoint. We applied the wordaspect relations into a sentence classification task. If theword-aspect relations is applicable, each sentence is classified
into the correct aspect. We used 700 test sentences withaspects that were annotated by handwork. Figure 5 shows anexample of the sentence classification task. In this task, we
computed the sum of the scores of words in each sentence
by using the word-aspect relations and scores by our method.
We selected the aspect that contains the maximum score as
the aspect of the sentence. The accuracy of the sentence
aspect identification was approximately 65%. Our method is
an unsupervised approach using structure information (SI)and rating information (RI). In other words, our method canidentify the aspect of a sentence with a non-tagged corpus.

TABLE I
T HE ACCURACY OF WORD - ASPECT RELATIONS.
Aspects

O
75%(15/20)56%(15/27)421

RecallPrecision
Words

G
90%
(18/20)90%(18/20)112

M100%(20/20)91%(20/22)
131

III. S UMMARIZATIONIn this section, we describe a method of review summarization with the word-aspect relations and scores explainedin the previous section. We apply an interactive approach tothe summarization process for the variety of users’ needs. Oursummarizer consists of the following three steps;
Step 1 : display words with high aspect scores for each
aspect,Step 2 : display words of the aspect that a user selects inthe step 1,
Step 3 : display sentences related to the word that a user
selects in the step 2.
In the 1st step and 2nd step, we visualize the data by using
the Tree-Map style [5]. The Tree-Map is a mapping approachwith rectangular regions in a space. The size of each rectangle
is based on the aspect score computed in Section II-B. In
other words, the size implies the number of mentions andthe importance of the word and aspect in the target review
documents.
First, a user selects an aspect in the Tree-Map in the 1st
step. Then, the system extracts words that are related to the
aspect from the word-aspect relation list. Next, the user selects
a word in the Tree-Map in the 2nd step. Our system outputs
sentences that contain the selected word in the target review
documents.
In the step 3, our system displays clustered sentences forthe readability. We use a  means based method that has
been proposed by Seki et al [13]. This method determines the
optimal  in the  means method. The method computes arate of change in    and  . We use a statistic proposedby Krzanowski and Lai [7] (in this paper, hereinafter referred
to as KL statistic). The statistic   where the number ofclusters is  is computed as follows:          (4)             (5)where  is the dimensions of the vector space.    is  computed as follows:

 




 

(6)

  is the summation of the sum of squares ( ) in a cluster.
  becomes small in the case that each cluster contains

A
75%(15/20)58%(15/26)386

S
45%(9/20)
82%(9/11)
157

C
75%(15/20)75%(15/20)356

D
45%(9/20)
75%(9/12)
225

similar words only. In the (6), thecomputed as follows:

 

Ü¾

  of a cluster  isÜ  Ñ (7)  is the sum of squares of the distance between the mean
vector (Ñ) and each vector (Ü) belonging to a cluster  . The
value ofis large if the variance of a cluster is large.
 is a criterion to evaluate a rate of change by
considering the dimensions of the vector space.   islarge in the case that     is large and     is small. In other words,  is large in the case that thevalues of the summation ofare large on the change from   to  and small on the change from  to   . They
concluded that the  is optimal because this denotes that theclustering process improves the output on the  as comparedwith the    and does not improve the output on the   
as compared with the  .
In this step, we visualize sentences by using the fisheyelike style [2]. The fisheye view is a distorted view methodof a data set. First we select a representative sentence that
contains the word in the 2nd step from each cluster. The
representative sentence is essentially the centroid of the vectors contained in the cluster. Then we compute a similarity
between the representative sentence and each sentence in thecluster belonging to the representative sentence. The similarity
between two sentences is computed by      (8)

where  is the number of morphemes matched between asentence  and a sentence  .  and  are the numberof morphemes in  and  . We display sentences with thefisheye style on the basis of the similarity.
Figure 6 shows an example of the summarization process.
In this example, the user selected the aspect “comfort” in the
1st step. Therefore, the system displayed 30 words belonging
to the selected aspect “comfort” in the 2nd step. Next, theuser selected the word “save” in the word list. In the 3rd step,
the summarizer classified sentences into several clusters, andextracted sentences that contained the word “save”. For theclustering process, our method tended to overdivide clusters.
The improvement of this problem is our future work. If theoutput is, however, not suitable for user’s needs, the user canobtain the summary that he/she wants by using the iterationof the step 2 and the step 3.

The 1st step: Words with high aspect scores and each aspect

Aspect name: Originality

High score words:- Game- System- Characters- Self.....

A user selects the aspect ``comfort’’

The 2nd step: Words belonging to the selected aspect

The size denotes the importance of
the word and aspect

The user selects the word ``Save’’* is the representative sentence in each cluster

The 3rd step: Summary - clustered sentences

English translation of this sentence
Anyway, there are limitations for ``Save’’ .

Similar sentencesandSimilar clusters* It allows ``save’’ at specific points only.
It is tough because we can not save the data freely.
We cannot save the data before eliminating the final boss.
Too limited save mode.
The save limitation really bothers me.
The inconvenient save system.

Fisheye-like outputs based on the similarity

Fig. 6.

English translation of this area

An example of the summarization process.

IV. D ISCUSSION AND C ONCLUSIONSIn this paper we described an aspect summarization system
for review documents. We identified aspects of each wordin the target review documents on the basis of two characteristics; structure information and rating information. The
average of the recall rate of the identification process was
72.1%. We also evaluated the word-aspect relations by our
method with a sentence classification task. The accuracy of thesentence aspect identification was approximately 65% withouta tagged corpus. However, the accuracy was insufficient. Titov
and McDonald [17] have also proposed a unsupervised learning method of aspect identification with Multi-Grain Latent
Dirichlet Allocation. Incorporating the related work into our
method might lead to the improvement of the accuracy.
Then, we summarized the review documents by using theword-aspect relations identified by a scoring process with thecharacteristics. In the summarization process, we applied aninteractive approach with the tree-map and fisheye-like styles.
Gamon et al. [4] have reported a visual summarization systemof product features. Carenini et al. [3] also have a visual summarization system with a natural language summary. Therestudies also applied the Tree-map style to the summarization.
Our system realized a readable summary by applying fisheyelike style to the natural language summary. In the output
process of related sentences, we used a  means based
clustering approach and a similarity measure. The clustering
method determined the optimal  automatically. However, it
tended to overdivide clusters. Therefore, we need to apply an
integration process of the overdivided clusters. The similarity
measure was based on the correspondence of morphemes. We
need to compare it with other similarity measures. Besides, weneed to quantitatively evaluate our method, e.g., the ROGUEN [8]. We have studied a static summarization task with thereview documents [16]. Applying the knowledge from theresearch into this dynamic and interactive summarization is
one future work.
ACKNOWLEDGMENT
This work was supported by Kayamori Foundation ofInformational Science Advancement.
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