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1. CO-ExtractingOpinionTargetsandOpinionWordsfromOnlineReviewsBasedOntheWord
AlignmentModel
OpinionMiningthroughco-Extractingprocessusingonlinereviews
Web BasedOpinionRelationsfromOnlineReviewsBasedontheWord AlignmentModel
S Mohammed Jabeer, P.Dilip Kumar
KORM College of Engineering,Kadapa
Abstract- Mining opinion targets and opinion words from
online reviews are important tasks for fine grained opinion
mining, the key component of which involves detecting
opinion relations among words. To this end, this paper
proposes a novel approach based on the partially-supervised
alignment model, which regards identifying opinion relations
as an alignment process. Then, a graph-based co-ranking
algorithm is exploited to estimate the confidence of each
candidate. We can say that this model obtains better precision,
As Compared to the traditional unsupervised alignment
model. When we search for candidate confidence, we get to
know that higher-degree vertices in the graph-based
algorithm are decreasing the probability of the generation of
error. Previous methods are based on syntax based, compared
to these methods proposed model minimizes negative effects of
parsing errors. Due to use of partial supervision proposed
model achieves better accuracy compared to unsupervised
word alignment model. Final task is to extractive summary
generation from Opinion Targets and Opinion Words with
Word Alignment Model. To precisely mine the opinion
relations among words, the Word Alignment Model (WAM) is
used and to progress the error propagation, the graph based
co-ranking algorithm is motivated. By comparing with the
syntax based method, the word alignment model effectively
reduces the parsing errors and the coranking algorithm
decreases the error probability.
Keywords: Opinion mining, Opinion target extraction,
Opinion word extraction, Text Mining.
1.INTRODUCTION
Data mining is the process of collecting, searching through, and
analysing a large amount of data in a database, as to discover
patterns or relationships. A series of challenges have emerged in
data mining and in that one of the major challenges is opinion
mining. Opinion mining is the field of study that analyses the
people opinions, sentiments, appraisals and emotion towards the
entities such as products, services. The main objective is to
gathering the opinion about the products from the online review
websites. The emergence of user-generated content via social
media had an undeniable impact on the commercial
environment. In fact, social media has shifted the content
publishing from business towards the customer. With the
explosive growth of social media for like microblogs, amazon,
flipkart. On the web, individuals and organizations are
increasingly using the content in these media for decision
making. Each site typically contains a huge volume of opinion
text. The average human reader will have difficulty in
identifying the relevant sites and extracting and summarizing
the opinions in them. So automated sentiment analysis systems
are needed.
Mining opinion targets and opinion words from online reviews
are important tasks for fine-grained opinion mining, the key
component of which involves detecting opinion relations among
words with the rapid development of Web 2.0, a huge number of
product reviews are springing up on the Web. From these
reviews, customers can obtain first-hand assessments of product
information and direct supervision of their purchase actions.
Means, manufacturers can obtain immediate feedback and
opportunities to improve the quality of their products. Thus,
mining opinions from online reviews has become an increasingly
urgent activity and has attracted a great deal of attention from
researchers .To extract and analyze opinions from online reviews,
it is unsatisfactory to merely obtain the overall sentiment about a
product. In most cases, customers expect to find fine-grained
sentiments about an aspect or feature of a product that is
reviewed.
With the rapid growth, of e-commerce, a number of customers
have taken interest in online shopping and more and more
products are sold on the web, so that there are a huge number
of product reviews are coming on the web. Online reviews are
often combined with numerical ratings provided by users for
product aspects. Reading through all customer reviews is
impractical, for popular items, as in some cases the number of
reviews can be up to hundreds or in some cases thousands. To
increase customer satisfaction, it has become a necessary for
online vendors to make their customers to review or to express
opinions about products.
These reviews are not only provide customers useful information
but also important for merchants to get immediate feedback from
customers immediately. The web contains number of opinions
about product, as a result the problem of opinion mining become
an increasingly important activity. Opinion mining has been an
important research area in NLP. Opinion mining is a type of
natural language processing for tracking the mood of public
about a particular product. Opinion mining, which is also called
sentiment analysis, involves building a system to collect and
categorize opinions about a product. Overall sentiment polarity of
a product is not just satisfied. In most cases, customers expect to
find fine grained sentiments about an aspect or feature of a
product that is reviewed
II LITERATURE SURVEY
We get to know that on the Internet, producer who sells the
manufactured goods,they tells the particular client to posting the
review with reference to that particular goods which they have
bought. Now these days, e-shopping is going in turn out to be
very popular along with famous.The client reviews statistics are
growing speedily day by day. If there is several trendy product,
then the reviews of that particular manufactured goods is in
hundreds or may be in thousands also. However this creates
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2. misunderstanding to the client if that particular product buy or
not. As well as it is also complicated to the producer of that
good if which product keeps in market.Same manufactured
goods are sold by many shopping sites. But this is very hard for
the producer of that product, because the job of the
manufacturer is only to produce dissimilar kinds of products. In
this subject, we examine all the review of manufactured goods
of all the dissimilar customers. From those we only examine the
pretend goods features on which product clients has given their
opinions. The opinions may be positive or sometime it may be
negative. In three steps our work is performed:First is that
manufactured goods features which are commented by the
clients those should be mine. After that Estimation sentences
Identification as well as makes a decision whether which
opinion sentence is affirmative or which is negative. And last
step is Results summarization.M. Hu and B. Liu[1] The pulling
out of reaction as well as subject lexicons these two belongings
are very significant for estimation taking out. We notice with
the purpose of in the preceding job, the supervise erudition
method be the most excellent. The recital of the supervise
methods mechanism on labeled facts which is physical. In this
theme, we include specified the variation outline wherever we
do not require any labeled records. Other than we require a lot
of labeled information. In the first walk, we produce aa small
number of high-confidence opinion and matter seed in the goal
sphere of influence. In the subsequent walk, we recommend a
work of fiction Relational bootstrapping algorithm.
Investigational outcome give you an idea about that our sphere
of influence construction can take out accurate lexicons in the
objective province.F. Li, S. J. Pan, O. Jin, Q. Yang,etal[2]
Pulling out of opinion of peoples secreted on features of a
personage is a necessary duty of termination withdrawal. Think
about succeeding occurrence, the judgment, “I like GPS
function of mobile” express a affirmative judgment on the GPS
utility of the phone. In the given judgment, GPS is the attribute.
This document focuses on drawing out features. In favor of to
resolve the difficulty, dual propagation is
introduced.Thismechanism fine for medium-size sector. On
behalf of bulky and tiny corpora, it is able to outcome in low
accuracy and low summon up. To agreement in the midst of
these two troubles, two improvements are introduced to
augment the call to mind. To get superior the exactness of the
twocandidates, feature position is useful to the extract
characteristic candidate. For status mark candidate by quality
value, it is resolute by two factors: quality significance and trait
incidence. The crisis is formulating as a bipartite chart and the
distinguished web page standing algorithm HITS. Experiment
on datasets gives you an idea about shows potential outcome.L.
Zhang, B. Liu, et al[3] On the network, peoples advertising
goods ask their clients to assessment the goods and coupled
services. As e-commerce is flattering extra admired, the amount
of purchaser review that a item for consumption receive grow
speedily. For a trendy invention, the quantity of review be able
to in hundreds. This makes it not easy for a probable buyer to
formulate a conclusion on whether to pay money for the
manufactured goods. In this plan, we intend to go over the main
points all the purchaser review of manufactured goods. This
summarization project is poles apart on or after conventional
content summarizations. We perform not recapitulate the review
by select a disconnection of the original sentence as of the
review. In this article, we just meeting point on deletion opinion
commodities features that reviewer comment. Figures of
technique are obtainable to supply some features. Our
investigational outcome shows with the intention of these
techniques are highly valuable.M. Hu and B. Liu[4] The opinion
glossary acting a key responsibility in the majority sentiment
investigation application. If it is not impracticable, to bring
together and keep up a universal response lexicon, subsequently
it is tough. Because of dissimilar terms may be used in poles
apart domain. The key existing method extracts such outlook
words from a bulky domain. In this manuscript, we recommend a
novel circulation approach that exploit the dealings between
response terms and topics or manufactured goods features. When
the process propagate information all the way through both
reaction words and features, then it consideration to be double
circulation. The pulling out rules are intended based on
associations described in reliance trees. A new technique is
projected to allocate polarity to recently discovered sentiment
lexis. Investigational results show that our come within reach of
is able to take out a large digit of new outlook words. The
polarization assignment process is also effectual.G. Qiu, B. Liu,
et al[5]
III. RELATED WORK
Level of Opinion Mining
The opinion mining tasks at hand can be broadly classified
based on the level at which it is done with the various levels
being namely,
 The document level,
 The sentence level and
 The feature level.
At the document level, sentiment classification of documents
into positive, negative, and neutral polarities is done with the
assumption made that each document focuses on a single
object O(although this is not necessarily the case in many
realistic situations such as discussion forum posts) and
contains opinion from a single opinion holder. At the sentence
level, identification of subjective or opinionated sentences
amongst the corpus is done by classifying data into objective
(Lack of opinion) and subjective or opinionated text.
Subsequently, sentiment classification of the aforementioned
sentences is done moving each sentence into positive,
negative and neutral classes. At this level as well, I make the
assumption that a sentence contains only one opinion which as
in our previous levels is not true in many cases. An optional
task is to consider clauses.
At the feature level, the various tasks that are looked at are:
been commented on in each review/text.
are positive, negative or neutral.
feature-based opinion summary of multiple reviews/text.
When both F (the set of features) and W (synonym of each
feature) are unknown, all three tasks need to be performed. If
F is known but W is unknown, all three tasks are needed, but
Task 3 is easier. It narrows down to the problem of matching
discovered features with the set of given features F. When
both W and F are known, only task 2 is needed.
The sentiment classification at the document-level is the most
important field of web opinion mining. However, for most
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3. applications, the document-level is too coarse. Therefore it is
possible to perform finer analysis at the sentence-level. The
research studies in this field mostly focus on a classification
of the sentences whether they hold an objective or a
subjective speech, the aim is to recognize subjective
sentences in news articles and not to extract them. The
sentiment classification as it has been described in the
document-level part still exists at the sentence-level; the
same approaches as the Turney's algorithm are used, based
on likelihood ratios. Because this approach has already been
described in this paper, this part focuses on the
objective/subjective sentences classification and presents
two methods to tackle this issue. The first method is based
on a bootstrapping approach using learned patterns. It means
that this method is self-improving and is based on phrases
patterns which are learned automatically.
The input of this method is known subjective vocabulary and
a collection of annotated texts.
• The high-precision classifiers find whether the sentences
are objective or subjective based on the input vocabulary.
High-precision means their behaviors are stable and
reproducible. They are not able to classify all the sentences
but they make almost no errors.
• Then the phrases patterns which are supposed to represent a
subjective sentence are extracted and used on the sentences
the HP classifiers have let unlabeled.
• The system is self-improving as the new subjective
sentences or patterns are used in a loop on the unlabeled
data.
This algorithm was able to recognize 40% of the subjective
sentences in a test set of 2197 sentences (59% are subjective)
with a 90% precision. In order to compare, the HP subjective
classifier alone recognizes 33% of the subjective sentences
with a 91%precision.Along this original method, more
classical data mining algorithm are used such as the naïve
bayes classifier. The general concept is to split each sentence
in features -- such as presence of words, presence of n-
grams, and heuristics from other studies in the field -- and to
use the statistics of the training data set about those features
to classify new sentences. Their results show that the more
features, the better. They achieved at best a 80-90%recall
and precision classification for subjective/opinions sentences
and a 50% recall and precision classification for
objective/facts sentences. The sentence-level sentiment
classification methods are improving, this results from
research studies in 2003 show that they were already quite
efficient then and that the task is possible.
Feature and Opinion Learner
This module is responsible to analyze dependency relations
generated by document parser and generate all possible
information components from them. The dependency relations
between a pair of words w1 and w2 is represented as relation
type (w1; w2), in which w1 is called head or governor and w2
is called dependent or modifier. The relationship relation type
between w1 and w2 can be of two types- i) direct and ii)
indirect. In a direct relationship, one word depends on the
other or both of them depend on a third word directly,
whereas in an indirect relationship one word depends on the
other through other words or both of them depend on a third
word indirectly. An information component is defined as a
triplet < f; m; o >, where f represents a feature generally
expressed as a noun phrase, o refers to opinion which is
generally expressed as adjective, and m is an adverb that acts
as a modifier to represent the degree of expressiveness of the
opinion. As pointed out in, opinion words and features are
generally associated with each other and consequently, there
exist inherent as well as semantic relations between them.
Therefore, the feature and opinion learner module is
implemented as a rule-based system, which analyzes the
dependency relations to identify information components
from review documents. For example, consider the following
opinion sentences related to Nokia N95:
(i) The screen is very attractive and bright.
(ii) The sound sometimes comes out very clear.
(iii) Nokia N95 has a pretty screen.
(iv) Yes, the push email is the Best" in the business.
In example (i), the screen is a noun phrase which represents a
feature of Nokia N95, and the adjective word attractive can be
extracted using nominal subject nsubj relation (a dependency
relationship type used by Stanford parser) as an opinion.
Further, using advmod relation the adverb very can be
identified as a modifier to represent the degree of
expressiveness of the opinion word attractive. In example (ii),
the noun sound is a nominal subject of the verb comes, and
the adjective word clear is adjectival complement of it.
Therefore, clear can be extracted as opinion word for the
feature sound. In example (iii), the adjective pretty is parsed
as directly depending on the noun screen through amod
relationship. If pretty is identified as an opinion word, then the
word screen can be extracted as a feature; likewise, if screen
is identified as a feature, the adjective word pretty can be
extracted as an opinion. Similarly in example (iv), the noun
email is a nominal subject of the verb is, and the word Best is
direct object of it. Therefore, Best can be identified as opinion
word for the feature word email.
The formal style has the following characteristics:
1. It uses an impersonal style: the third person (“it”, “he” and
“she”) and often the passive voice (e.g., “It has been noticed
that...”).
2. It uses complex words and sentences to express complex
points (e.g., “state-of-the-art”).
3. It does not use contractions.
4. It does not use many abbreviations, though there are some
abbreviations used in formal texts, such as titles with proper
names (e.g., “Mr.”) or short names of methods in scientific
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4. papers (e.g., “SVM”).
5. It uses appropriate and clear expressions, precise
education, and business and technical vocabularies (Latin
origin).
6. It uses polite words and formulae, such as “Please”,
“Thank you”, “Madam” and “Sir”.
7. It uses an objective style, citing facts and references to
support an argument.
8. It does not use vague expressions and slang word
IV.PROPOSEDWORK
In this, we can present a feature-based product ranking
technique that mines various customer reviews. We first
identify product features and analyze their frequencies. For
each feature, we identify subjective and comparative
sentences in reviews. We then assign sentiment orientations
to these sentences. We model the relationships among
products by using the information obtained from customer
reviews, by constructing a weighted and directed graph. We
mine this graph to determine relative quality of products.
Experiments on Digital Camera and Television reviews
demonstrate the results of the proposed techniques.
Because of the user convenience as well as reliability, and
the product cost there are the large numbers of customers
are choosing one of the best way to online shopping online
shopping. Andnow a days, online shopping is much more
popular in the world. And this makes very profitable to
customer. To make purchasing the decisions is based on
only pictures and short descriptions of the product, and it is
very difficult for customers to purchasing the customers; as
the number of products being sold online is increases. On
the other hand, customer reviews, i.e. text describing
features of the product, their comparisons and experiences
of particular product provide a rich source amount of
information to compare products. And to make the good
purchasing decisions, online retailers like Amazon.com,
and flipcart.com allow us customers to add reviews of
products that they have purchased. These reviews become
diverse to aid the other customers. Traditionally, many
customers have used expert rankings. To assign the rank to
the product, then it is very beneficial for the customer to
select the product and its quality like good in quality or
bad. Moreover,the product usually has multiple product
features, their advantages and some drawbacks, which
plays a vital role in different manner. Different customers
may be interested in different features of a product, and
their preferences may vary accordingly.
IVMETHODOLOGY
In this section, we present the main framework of our
method i.e. extracting opinion targets/words as a co-ranking
process. We assume that all nouns/noun phrases in
sentences are opinion target candidates, and all
adjectives/verbs are regarded as potential opinion words,
which are widely adopted by previous methods [iv], [v],
[vii], and [vii]. Each candidate will be assigned a
confidence, and candidates with higher confidence than a
threshold are extracted as the opinion targets or opinion
words. If a word is likely to be an opinion word, the nouns/
noun phrases with which that word has a modified relation
will have higher confidence as opinion targets. We can see
that the confidence of a candidate (opinion target or opinion
word) is collectively determined by its neighbours according
to the opinion associations among them of each candidate.
Existing system on opinion mining have applied various
methods for extracting opinion targets and opinion words.
[i]. Extracting opinion targets and opinion words using word
alignment model using partially supervised word alignment
model [i]:
The proposed method contains three main modules. They are
pre-processing, opinion target and word extraction and
opinion word classification. The overall diagram of the
proposed method is shown in Fig.1. In pre-processing the
given comment is processed and eliminates stop word and
stemming. Extracting opinion targets/words as a co-ranking
process. Assume all nouns/noun phrases in sentences are
opinion target candidates, and all adjectives/verbs are
regarded as potential opinion words. And then the opinion
word is classified as good or bad.
A. Pre-Processing This is the first step of the proposed
method. Several preprocessing steps are applied on the given
comment to optimize it for further experimentations. The
proposed model for data pre-processing is shown in Fig.2.
Tokenization process splits the text of a document into
sequence of tokens. The splitting points are defined using all
non-letter characters. This results in tokens consisting of one
single word (unigrams). The movie review data set was
pruned to ignore the too frequent and too infrequent words.
Absolute pruning scheme was used for the task. Length
based filtration scheme was applied for reducing the
generated token set.
The parameters used to filter out the tokens are the minimum
length and maximum length. The parameters define the
range for selecting the tokens. Stemming defines a technique
that is used to find the root or stem of a word. The filtered
token set undergoes stemming to reduce the length of words
until a minimum length is reached. This resulted in reducing
the different grammatical forms of a word to a single term.
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5. Fig. 1. Overall Diagram of the Proposed Method
B. Opinion Targets and Opinion Words Extraction The
modified word alignment model assumes that all
nouns/noun phrases in sentences are opinion target
candidates, and all adjectives/verbs are regarded as
potential opinion words. A noun/noun phrase can find its
modifier through word alignment.
The proposed word alignment model apply a
partiallysupervised modified word alignment model. It
performs modified word alignment in a partially supervised
framework. After that, obtain a large number of word pairs,
each of which is composed of a noun/noun phrase and its
modifier. And then calculate associations between opinion
target candidates and opinion word candidates as the
weights on the edges. The modified word alignment model
example is shown in Fig.2.
Fig. 2. Process Flow Diagram of Preprocessing
C. Opinion Words Classification After extraction opinion
word and target the next step is to classify the opinion
word. In this process the opinion word is classified as good
or bad. The knn classifier is used to classify the opinion
word. Once it is classified as bad then the comment is
removed. In k-NN classification, the output is a class
membership. An object is classified by a majority vote of its
neighbors, with the object being assigned to the class most
common among its k nearest neighbors (k is a positive
integer, typically small). If k = 1, then the object is simply
assigned to the class of that single nearest neighbor.
IV. CONCLUSION
We studied a novel method by making use of word alignment
model, for co-extraction of opinion targets as well as co-
extraction of opinion words. The main goal is to focusing on
detection of the opinion relations which are present in
between opinion targets and opinion words. As compared with
previous method which is based on nearest neighbor rules and
syntactic patterns, this proposed method captures opinion
relations. Because of this advantage, this method is more
useful for extraction of opinion target and opinion word. This
paper proposes a novel method for co-extracting opinion
targets and opinion words by using a word Alignment model.
Our main contribution is focused on detecting opinion
relations between opinion targets and opinion words.
Compared to previous methods based on nearest neighbor
rules and syntactic patterns, in using a word alignment model,
our method captures opinion relations more precisely and
therefore is more effective for opinion target and opinion
word extraction. Next, we construct an Opinion Relation
Graph to model all candidates and the detected opinion
relations among them, along with a graph co-ranking
algorithm to estimate the confidence of each candidate. The
items with higher ranks are extracted out. The experimental
results for three datasets with different languages and different
sizes prove the effectiveness of the proposed method. In
future work, we plan to consider additional types of relations
between words, such as topical relations, in Opinion Relation
Graph. We believe that this may be beneficial for coextracting
opinion targets and opinion words.
REFERENCE
[1]M. Hu and B. Liu, “Mining and summarizing customer
reviews,” in Proc. 10th ACM SIGKDD Int. Conf. Knowl.
Discovery Data Mining, Seattle, WA, USA, 2004, pp. 168–
177.
2]F. Li, S. J. Pan, O. Jin, Q. Yang, and X. Zhu, “Cross-
domain coextraction of sentiment and topic lexicons,” in Proc.
50th Annu. Meeting Assoc. Comput. Linguistics, Jeju, Korea,
2012, pp. 410419.
[3]L. Zhang, B. Liu, S. H. Lim, and E. O’Brien-Strain,
“Extracting and ranking product features in opinion
documents,” in Proc. 23th Int. Conf. Comput. Linguistics,
Beijing, China, 2010, pp. 1462–1470.
[4] M. Hu and B. Liu, “Mining opinion features in customer
reviews,” in Proc. 19th Nat. Conf. Artif.Intell., San Jose, CA,
USA, 2004, pp. 755–760.
[5] G. Qiu, B. Liu, J. Bu, and C. Che, “Expanding domain
sentiment lexicon through double propagation,” in Proc. 21st
Int. Jont Conf. Artif. Intell., Pasadena, CA, USA, 2009, pp.
1199–1204.
[6] X. Ding, B. Liu, and P. S. Yu, “A holistic lexicon-based
ISBN-13: 978-1537584836
www.iaetsd.in
Proceedings of ICAER-2016
©IAETSD 201653

6. approach to opinion mining,” in Proc. Conf. Web Search
Web Data Mining, 2008, pp. 231–240.
[7] F. Li, C. Han, M. Huang, X. Zhu, Y. Xia, S. Zhang, and
H. Yu, “Structure-aware review mining and summarization.”
inProc.23thInt.Conf.Comput.Linguistics,Beijing,China,2010,
pp.653–661.
[8] B. Wang and H. Wang, “Bootstrapping both product
features and opinion words from chinese customer reviews
with crossinducing,” in Proc. 3rd Int. Joint Conf. Natural
Lang. Process., Hyderabad, India, 2008, pp. 289–295.
[9] F. Li, C. Han, M. Huang, X. Zhu, Y. Xia, S. Zhang, and
H. Yu, “Structure-aware review mining and summarization.”
in Proc. 23th Int. Conf. Comput. Linguistics, Beijing, China,
2010, pp. 653–661.
[10] T. Ma and X. Wan, “Opinion target extraction in
Chinese news comments.” in Proc. 23th Int. Conf. Comput.
Linguistics, Beijing, China, 2010, pp. 782–790.
[11] W. X. Zhao, J. Jiang, H. Yan, and X. Li, “Jointly
modeling aspects and opinions with a MaxEnt-LDA hybrid,”
in Proc. Conf. Empirical Methods Natural Lang. Process.,
Cambridge, MA, USA, 2010, pp. 56–65.
[12] Z. Liu, X. Chen, and M. Sun, “A simple word trigger
method for social tag suggestion,” in Proc. Conf. Empirical
Methods Natural Lang. Process., Edinburgh, U.K., 2011, pp.
1577–1588.
[13] Q. Gao, N. Bach, and S. Vogel, “A semi-supervised
word alignment algorithm with partial manual alignments,”
in Proc. Joint Fifth Workshop Statist. Mach. Translation
MetricsMATR, Uppsala, Sweden, Jul. 2010, pp. 1–10.
[14] K. Liu, H. L. Xu, Y. Liu, and J. Zhao, “Opinion target
extraction using partially-supervised word alignment
model,” in Proc. 23rd Int. Joint Conf. Artif. Intell., Beijing,
China, 2013, pp. 2134–2140.
[15] Z. Hai, K. Chang, J.-J. Kim, and C. C. Yang,
“Identifying features in opinion mining via intrinsic and
extrinsic domain relevance,” IEEE Trans. Knowledge Data
Eng., vol. 26, no. 3, p. 623–634, 2014.
[16] J. Zhu, H. Wang, B. K. Tsou, and M. Zhu, “Multi-
aspect opinion polling from textual reviews,” in Proc. 18th
ACM Conf. Inf Knowl. Manage., Hong Kong, 2009, pp.
1799–1802.
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