1. International Journal of Advanced Research in Engineering RESEARCH IN ENGINEERING
INTERNATIONAL JOURNAL OF ADVANCED and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME
AND TECHNOLOGY (IJARET)
ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 4, Issue 7, November - December 2013, pp. 176-182
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IJARET
©IAEME
AN OVERVIEW OF OPINION MINING TECHNIQUES
Dr. Jamshed Siddiqui
Department of Computer Science, Aligarh Muslim University, Aligarh, U.P.
ABSTRACT
The world with an intense increase in the changing technologies and due to the rapid growth
in the Internet, facing changes dramatically and created the scenario that life style of an individual
has also got changed. Users use Internet tools like blogs, social networking sites etc. to share their
views and opinions for various daily life issues that vary from on-line marketing and reviewing the
product to the election campaign and views of a voter for their electoral candidate. Opinion Mining
is required for such situation to infer the correct results and predict the future behavior. In this paper,
a short review to various opinion mining techniques is performed with tables depicting the
contribution in recent times with figures illustrations. Various techniques used, Corpus description
and basic concepts are also discussed. This work may be useful for the researcher to get a
background of opinion mining and finding future trends.
Keywords: Blogs, Opinion Mining, Internet, Social Networking, Corpus.
I. INTRODUCTION
The celerity in the growth of the Internet has a diverse effect in the daily life of a common
man. The world with an intense increase in the changing technologies and due to the rapid growth in
the Internet, facing changes dramatically and created the scenario that life style of an individual has
also got changed. Because Internet is being used commonly, the social lives are also affected. Users
use Internet tools like blogs, social networking sites etc. to share their views and opinions for various
daily life issues that vary from on-line marketing and reviewing the product to the election campaign
and views of a voter for their electoral candidate. Opinions of one influence other and play a key role
in deciding the behavior. Almost all of us take help from other ideas and views to decide own way to
proceed, individuals would like to know the opinions of their family and friends, organizations use
opinion polls, conduct surveys and hire consultants for making their strategies. The need of opinion
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2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME
mining emerges in such a situation to infer the right choice and to get benefited by the other
experiences and views. In last few decades, the opinion mining has gained much popularity [1].
Opinion mining can be considered as a computational study of the opinions [2]. The sources
of the opinion may be the social networking sites like Facebook and twitter, feedback from emails of
the employee of an organization, opinions in news articles, blogs, product web sites where thousands
of user generated free text are available, etc.
Inferring the true aspect of an opinion is not an easy task, sometimes a very confusing state is
created. The same object may be perceived differently by different people. For an example let us
consider Mahatma Gandhi, for India he is non-violent, father of the nation but British ruler would
refer him as their enemy. So the main task in opinion finding is to consider all these aspect and to
infer right thing according to the situation for the target customer that may be a newspaper reader
and an online-customer even a voter who wishesto vote for his choice of candidate.
Rest of the paper is organized as follows. The second section reviews the literature and in the
third section the different aspects of data mining is elaborated. Fourth section concludes the
summary.
II. LITERATURE REVIEW
Data Mining is one of the esteemed branch in computer science which was started initially
for business purposes[3], with timeits area of research is going to spread over medical sciences,
scientific research and social networking.[4]. Opinion mining is one of the most emergingbranch of
the data mining widely being used worldwide, interchangeably termed as sentiment analysis[2].
Themining of direction-based was first proposed by Hearst and Wieb that may include biases [5].
Opinion mining is done in text analysis and is similar as to find positive negative text, that can also
be done by supervised and unsupervised learning as well [6].
Reviews and opinions of user generated freetext plays an important role in sentiment analysis
[7] the contribution in identifying review in heterogeneous groups of text was made by Finn[8], it
simply implies subjectivity in [9] tried to overcome the difficulties in getting the similarities and
differences in the connoted word. The technicality in the similarity between two words was also
described by [10].
In [11, 12, 13] authors tried to give a solution for overly specific problem in subjectivity
identification. The manual annotation has two main issues; time consuming and expensive. [14, 15,
16] for linguistic corpora, a research based upon corpus is very useful. MPQA is one of these kinds
of existing corpus. But annotation was not based upon sentence level, but only word level[17]. In
[18] the contribution of Pang corpus generation is also discussed.For1 million English word, an
international corpus of English is supposed to be launched soon [19], A detail discussion of opinion
mining is performed in [20]; subtask in TREC blog track is one of the examples of opinion retrieval.
Feature based opinion summarization of reviews was suggested byHu and Liu [21].
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3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME
1.
Yi Fangy, LuoSiy,
Naveen
Somasundaramy,
ZhengtaoYuz
2012
Basis of
Research
Cross-prospective Query Topic,
Topic Model
Text
Collections
2.
Daniel E. O'Leary
2011
Reviewing Blogs
Blogs
Extensive review on the
topic
3.
PawelSobkowicz,
Michael Kaschesky,
Guillaume Bouchard
2012
Opinion
formation
framework
previous
research, online
opinions
emerge
diffusion
emotion and opinion
detection, model of
opinion network,
information flow
modeling, agent base
simulation
4.
KaiquanXu, Stephen
Shaoyi Liao, Jiexun
Li, Yuxia Song
2010
Graphical Model
to extract and
visualize
customer
generated
reviews
extract customer reviews,
visualization, analyze
customer generated data
5.
M. Eirinaki, S. pisal,
J. Singh
2011
sentiment
analysis and
semantic
orientation
algorithm
reviews, search
engine
Analysis of sentiments,
Semantic Orientation,
Opinion search engine.
6.
AmnaAsmi and
TankyoIshaya
2012
Auto generation
of corpus
user generated
content,
WorldNet
generation of corpus
7.
S.S. Patil and A.P.
Chaudhary
2012
SVM
classification
Corpus,
reviews
Emotion classification in
6 categories
8.
Kushal Dave, Steve
Lawrence, David M.
Pennock
2003
Reviews
Classifiers
Product
Reviews
Distinguishing positive
and negative reviews ,
grouping sentiments into
attribute
Sl.No.
Authors
Year
Proposed Model
Contribution
Opinion of individual
perspective on the topic,
Quantifying opinion
differences
III. OPINION MINING TECHNIQUE
The term opinion mining is similar to the sentiment analysis and both are widely used in
academia, though in industry sentiment analysis is used extensively[2]. In general, there are two
types of opinions:
1) Regular opinions
2) Comparative opinions
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4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME
In regular opinion, sentiments on some target entities are expressed; simply they are either
negative or positive expressions of their opinions for some specific aspects. There are two sub
categories:
a) Direct opinion
b) Indirect opinion
Direct opinion involves direct statement like, “the book is very interesting and knowledgegiving”, indirect statement involves indirect expressions like, “after reading the book, I fill the
bucket of my knowledge”.
In comparative opinions, two are more entities are compared with each other, e.g. “MCQ
physics by Upkar is better than Objective Physics by Sanjeev Gupta.” The term entity refers to
products in general, this product can be a person, an organization or even a topic of discussion. The
entity ‘product’ is associated with a set of attributes called node if represented graphically. One can
express opinion on any attribute of the node. Depicted in figure 1, university is an organization that
has several branches, here branches refer faculties. And these branches have nodes, for our example
these are departments. These can be considered as the components of the attributes university. So
concluding about university i.e. entity or attribute may need knowing the opinion about the
department i.e. components. The term aspect or feature refers to both attributes and components.
University
Faculty
Faculty
Faculty
Faculty
(Arts)
(Soc. Sc.)
(Science)
(Medical)
)
Departments
Hindi
English
Physics
Computer
Urdu
Geology
Figure 1. Opinion Mining for different branches of an item
IV. CRITERIA FOR OPINION MINING
The opinion mining can be categorized on several bases, extracting features from opinion and
inferring the conclusion from those opinions is a very important aspect in the field of sentiment
analysis. Therefore feature based opinion mining is very important and plays a vital role in sentiment
analysis. Feature based opinion mining involves following steps:
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5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME
IV. I Reviews Collection
The collections of reviews are also very important. The source for these reviews decides the
content and accordingly the decision is made. Blogs, Social networking site, News, emails, Products
web sites etc. are the main sources in the Internet for the reviews collections.
IV. II Feature Extraction
Features extraction is one of the important task in opinion mining, in [s] gave a formulation
based method to extract opinion. Though the work was done manually, however features extraction
method is also discussed in [22], where author discussed two types of features:
a) Implicit Feature
b) Explicit Feature
Implicit features involve those features of the products which users give in some specific
form, like in adjective form. The sentences in the reviews as described below could be implicit.
The book is too huge to read
Explicit features involve the features of a product described in Noun form, like:
The book is precise and interesting
There are features pruning as well so that to remove unnecessary features not essential and
probably may be incorrect. A diagrammatic representation of opinion mining is depicted in the
figure 2.
Mining
Review/Blog
User
Sentiment
Inference
Corpus
Figure 2. Overview of Opinion Mining Technique
IV. III Inferring Results
After the extraction of features, the inference of conclusion is the major task that depends
upon the experimental work done and the parameters and algorithms developed. There are various
statistical parameters and different classifiers that is usualy used for the purpose. In [patiliaeme], they
discussed various classifiers, Support Vector Machine (SVM), Vector Machine (VM) and NAVIE
etc.
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6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME
V. CONCLUSION
We usually take help from other ideas and views to decide own way to proceed, individuals
would like to know the opinions of their family and friends, organizations use opinion polls, conduct
surveys and hire consultants for making their strategies. The need of opinion mining emerges in such
a situation to infer the right choice and to get benefited by the other experiences and views. In this
paper the importance of user generated reviews including the description of existing corpus and
technologies used to extract features from these reviews are discussed. Different types of opinion
mining and techniques are also discussed. In future these work would help the research to find the
coming challenges and to contribute with a high note in the filed of opinion mining.
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