Product Opinion Mining for Competitive Intelligence

Abstract

Competitive Intelligence is one of the keys of companies Risk Management. It provides the company with a permanent lighting to its competitive environment. The increasingly frequent use of Information and Communication Technologies (ICT); including (namely) online shopping sites, blogs, social network sites, forums, provides incentives for companies check their advantages over their competitors. This information presents a new source that helps and leads the company to identify, analyze and manage the various risks associated with its business/products. Nowadays, a good use of these data helps the company to improve its products/services. In this paper, an overview of opinion mining for competitive intelligence will be presented. We’ll try to synthesize the major research done for the different steps of product opinion mining.

Full text

Procedia Computer Science 73 ( 2015 ) 358 – 365
1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of organizing committee of the International Conference on Advanced Wireless, Information, and Communication
Technologies (AWICT 2015)
doi: 10.1016/j.procs.2015.12.004
ScienceDirect
Available online at www.sciencedirect.com
The International Conference on Advanced Wireless, Information, and Communication
Technologies (AWICT 2015)
Product Opinion Mining for Competitive Intelligence
Kamal AMAROUCHE, Houda BENBRAHIM, Ismail KASSOU
ALBIRONI Research Team, ENSIAS, Mohammed 5 University, Rabat, Morocco
Abstract
Competitive Intelligence is one of the keys of companies Risk Management. It provides the company with a permanent lighting
to its competitive environment. The increasingly frequent use of Information and Communication Technologies (ICT); including
(namely) online shopping sites, blogs, social network sites, forums, provides incentives for companies check their advantages
over their competitors. This information presents a new source that helps and leads the company to identify, analyze and manage
the various risks associated with its business/products. Nowadays, a good use of these data helps the company to improve its
products/services. In this paper, an overview of opinion mining for competitive intelligence will be presented. We’ll try to
synthesize the major research done for the different steps of product opinion mining.
© 2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of organizing committee of the International Conference on Advanced Wireless, Information,
and Communication Technologies (AWICT 2015).
Keywords: Competitive Intelligence; Opinion Mining; Opinion Classification; Machine Learning; Natural Langage Processing
1. Introduction
Competitive intelligence (CI) seeks to analyze and exploit information about companies’ competitors and sectors
of activity to determine its competitive strategy. Actually, companies must be able to develop new knowledge about
its competitors in an increasingly complex and fast-moving economy to maintain levels of innovation and gain a
competitive advantage. Therefore, the importance of CI in companies practically becomes a necessity and widely
accepted1.
Traditionally, information about competitors has mainly come from press releases, such as analyst reports and
trade journals, and recently also from competitors’ websites and news sites. Unfortunately, the amount of this
available information is limited and its objectivity is questionable. The lack of sufficient and reliable information
sources about competitors greatly restricts the capability of CI2. But Nowadays, the consumer reviews or opinions
about an event, a product or a topic are available via forums, newsgroups, weblogs, and other similar sources. There
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of organizing committee of the International Conference on Advanced Wireless, Information,
and Communication Technologies (AWICT 2015)

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Kamal Amarouche et al. / Procedia Computer Science 73 ( 2015 ) 358 – 365
is also an easy access to virtually all sources of traditional public media such as electronic Daily news and
magazines. This information may be analyzed in order to facilitate the monitoring of the competitive environment of
a company. Manually, this task is difficult; this is why the automatic methods were used to alleviate humans from it.
Many automatic methods aim to solve many issues in CI such as (i) how will our new product compare to our
competitors’ products? or (ii) which key factors influence customers’ decisions to buy from our competitors or us?
or (iii) which competitive factors affect our growth strategy? In this paper, we will focus on the automatic analysis
of customers’ opinions about products for the competitive intelligence context. Thus, product Opinion mining
techniques for competitive intelligence will be discussed. This paper is organized as follows. Section 2 presents
some definitions, processes and sources of competitive intelligence. Section 3 provides an overview of product
opinion mining for competitive intelligence. Finally, section 4 provides a conclusion to the paper.
2. Competitive Intelligence (CI)
Facing competition, every company seeks to monitor its competitor markets, advertising marketing actions and
information power of selling (evolution of turnover, logistics). All of these lead to the emergence of competitive
intelligence. In this section, we will first start by defining the CI. The second part will present its process and the last
part will present its information sources.
2.1. Definition
An exact definition of Competitive Intelligence can’t be found in the literature, because definitions vary
according to different authors and approaches in the business field.
According to Bartes3, CI seeks to predict the future, and the strategic company decisions based on these
predictions. Ĺubica1 defined competitive intelligence as the process of monitoring the competitive environment and
the competitors, in which, information gathering, analysis and distribution of the obtained results, is carried out
gradually so that they can support the efficient business activity and its ability to make qualified decisions,
especially in relation to its competitors.
Safarnia4 reported that competitive intelligence is an activity focused on the understanding of competitors, their
strengths, weaknesses and expectations of their actions. CI, according to them, is wider and includes activities to
understand the competitive environment in relation to own business, the analysis of the impact of competition on
business and the possible actions and reactions of the enterprise. Various authors distinguish between even three
different views1, namely, (i) competitive intelligence is equal to Business Intelligence, (ii) Competitive Intelligence
is part of Business Intelligence, (iii) Competitive Intelligence is understood as relatively a separate information
system. The first view is mainly encountered in the American literature1, where the two concepts are understood as
synonyms. The second view presents CI as part of the parent category Business Intelligence5, which is understood as
a group of resources and ideas supporting all areas of management decision-making with an emphasis on improving
the awareness of managers at all levels of management. The last view is presented by Špingl6 and he says that while
CI is more focused on external environment, primarily on the behavior of competitors, BI focuses primarily on
indoor environments. In other words, BI is working with information that is within the company (even the external
environment). CI works mainly with information that is outside of the company.
Pellissier & Nenzhelele5 defined competitive intelligence as a product, a service and a process. As a product, CI
is a system of strategic and organizational information. As a service, CI is cartography of the business environment.
As a process, CI is the workflow of strategic management of information for collaborative decision making
consisting of phases that are linked. The output of each phase is the input to the next phase. The overall output of the
CI process is an input to the decision-making process.
As a summary and based on these different definitions, we can state that competitive intelligence is the research
and information processing in relation to the enterprise market, so that a company can prepare future actions based
on these analysis.

360 Kamal Amarouche et al. / Procedia Computer Science 73 ( 2015 ) 358 – 365
2.2. Process
Competitive intelligence does not attempt to collect and analyze all information for an exact picture, but attempts
to get enough information so that we can tell what is going on. It is like a picture that is out-of-focus. We need to
analyze enough details so that we can discern the big picture and report it to management. Therefore, competitive
intelligence does not chase down all the facts, but gets enough information to draw a reasonable conclusion for
immediate action.
Among the competitive intelligence processes that exist in the literature, There is PCMAC7 (Plan and prioritize,
Capture, Manage, Analyze and Communicate) model that consists of the following phases:
x Plan and prioritize – where the work is planned, resources allocated and the key intelligence topics and
questions are identified.
x Capture – where the information is collected.
x Manage – where the collected information is filtered, sorted and compiled.
x Analyze – where the analyses are done and the dots joined up.
x Communicate – where the result is disseminated to the target groups and archived for further use.
Another process that contains five steps8:
x Identification of CI needs – Identification of the key intelligence topics and the determination of the course
the CI practitioner should take in completing the analysis.
x Acquisition of Competitive Information – Information collection of CI from different sources.
x Organization, Storage, and Retrieval –organize and store the information for CI.
x Analysis of Information – Brain of the CI system that transforms information into intelligence.
x Dissemination.
According to the definitions of competitive intelligence, we can notice that its main objective is to prepare the future
actions based on the outputs of the information analysis phase. Therefore, the time axis, during this process to make
a decision at the right time, reveals to be very important. So I propose to include time in the general process of CI.
Time dimension should be taken into consideration along all the standard phases of CI. Figure Fig.1 summarizes
the proposed process.
Fig. 1. Process of Competitive Intelligence
2.3. Information Sources
The information research can start in the prioritized primary and secondary sources7. The primary information
sources are the well known phenomenon that much of the information needed in an organization is already there. It
is, however, kept with different people and it is very difficult to get an overview of who knows what. One way to
capture this tacit information is to participate in networks. Secondary information sources, covering explicit
information, comprise not only various websites and databases, social media, newspapers, journals, reports and
books but also the unstructured mass of e-mails, memos, forms, faxes...
Previously, the problem that limits the functions of competitive intelligence is the lack of information sources,
while nowadays with the emergence of web 2.0, the information about competitors can be accessed by the public on
the web. E.g. the product opinions coming directly from customers are a source of information for CI. So we need to
exploit and analyze these opinions to help the Leader of a company at the right time to identify potential risks that
might support the strategic decisions.

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Several researches have been carried out to take advantage of those opinions in the area of competitive
intelligence. To mention a few, Kaiquan Xu2 proposed a graphical model that works on customer opinions for some
comparative products to visualize the relationships between them and extract the relationship for enterprise risk
management. Wu He9 uses text mining to perform competitive analysis for the user-generated data on Twitter and
Facebook in three major pizza chains.
The application of opinion analysis in many areas is also important for companies to monitor, e.g. the advertising
or marketing activities of competitors or detect Competitors’ products news.
3. Product Opinion Mining
Currently, E-commerce websites and social media play a very important role in different sectors and different
businesses. For example, a company that wants to know the position of its products compared to its competitors
insight exploits customers opinions.
Opinion Mining (OM) or Sentiment Analysis (SA) is a process that analyses the conversations around an event, a
topic or a product, based on a system that automates this process. Among the tasks of opinion mining we can
mention subjectivity analysis10, affect analysis11, emotion analysis12, and the contextual polarity (positive or
negative) of a document or comment13. In this paper, we will focus on product opinion mining where the polarity of
the opinion concerning a product’s feature or characteristic will be examined.
An opinion has five main components14, i.e. ሺ݋௝ǡ݂
௝௞ǡݏ݋௜௝௞௟ ǡ݄
௜ǡݐ
௟ሻ where:
x ݋
௝ is a target object about which the opinion is expressed. An object ݋
௝ is a person, event, product,
organization, or topic,
x ݂
௝௞ is a feature of the object ݋
௝ ,
x ݏ݋௜௝௞௟ is the sentiment value of the opinion of the opinion holder ݄௜ on feature ݂
௝௞ of object ݋
௝ at time ݐ௟,
x ݄௜ is an opinion holder or source of the opinion,
x ݐ௟ is the time when the opinion expressed.
For opinion identification, all of these components are important.
Actually, opinion mining (OM) is a field of text mining15. Its purpose is to classify a comment to being either a
positive or negative opinion. Therefore, opinion mining can be projected to a binary text classification problem
while taking into consideration some characteristics of OM problems. It can, then, have the same building blocks of
a text classification system.
The process starts with data collection. Many sources like blogs, social media and web news contain products
opinions. In general a comment or an opinion has to be pre-processed to map the text comment to a representation
suitable for the automatic classification, then feature identification and extraction follows, finally, comment’s
polarity classification is performed as shown in Fig 2.
Fig. 2. Opinion mining process
3.1. Preprocessing
The text documents that contain an opinion must be preprocessed and stored in an appropriate data structures for
further processing. Usually, these opinions contain several syntactic features that may not be useful for the next
steps. These opinions need to be tokenized, normalized then cleaned. Some advanced processing might be
performed on text opinions, to name a few, normalization, grouping of synonyms and spelling errors checking.

362 Kamal Amarouche et al. / Procedia Computer Science 73 ( 2015 ) 358 – 365
x Normalization is realized by finding and removing word suffixes that are connected to inflection, in order to
identify and consider different occurrences of the same term independently of their role in the context of the specific
sentences in which they are used (e.g., ‘computer’ and ‘computers’ convey the same meaning, as well as
‘computing’ and ‘computed’). This can be an important task if machine learning approach is used later. Two kinds
of normalization are available16: stemming reduces a term to its root, which is not necessarily a meaningful word in
the language (e.g., ‘computer’, ‘computers’, ‘computing’ and ‘computed’ would all be reduced to the same stem
‘comput’); lemmatization transforms the term to its basic form, depending on its grammatical type (so, e.g.,
‘computer’ and ‘computers’ would be changed to ‘computer’, while ‘computing’ and ‘computed’ would be changed
to ‘compute’).
x Grouping of synonyms: The problem encountered is that the same meaning of a word can be expressed by many
different words, e.g. 'image', 'photo' and 'picture' have the same meaning for phone products. To solve this problem
we need to group these words to facilitate feature extraction step. Among the works which deal this problem we find
Zhongwu Zhai17 that uses a semi-supervised method based on WordNet18 in order to group these synonyms.
x Spelling check and correctors: There are multiple approaches and techniques for spell checking that can be
classified into context-independent and context-dependent error corrections19. The approaches for context-
independent correction execute words correction independently of the context by using probabilistic techniques or
neuronal network ones... The approaches for context-dependent error correction carry out a correction according to
the contextual information available using machine learning or semantic distance ones ...
3.2. Feature Extraction
Feature Extraction depends on the application domain, for example features of an “image” in image processing
field are: contrast, intensity, luminosity. However, products opinion mining characteristics (e.g. telephone features)
are: battery life, picture, and camera. This is an important step in product opinion mining. This is an important step
in product opinion mining that can be classified into four categories: machine learning, ontology, lexicon-based and
dependency-relation-based approaches.
x Machine learning feature extraction approaches: Product features are normally nouns or noun phrases. The
machine learning approach mainly relies on probabilities of these nouns and noun phrases being product feature
terms calculated based on their occurrence frequencies. Hu et al. 20 applied association rule mining to extract
product features from reviews. Jin21 proposes a supervised learning method based on lexicalized Hidden Markov
Models (L-HMMs) based on linguistic features.
x Ontology feature extraction approaches: An ontology is used in order to extract the features included in the
opinions. Moreno, V. 22 uses domain ontology to identify the features of opinions expressed by users (application
areas: movie), but with the rapid increase and variety of online products, we can’t always find an ontology for a
specific product that poses a problem to its use.
x Lexicon based feature extraction approaches: This type of approach extracts product features based on a lexicon.
Li et al. 23 constructed a lexicon that contained a list of feature words and opinion words and used it to assign a
polarity tag to each feature. Then a NLP techniques and statistical methods are applied to extract feature words and
opinion words based on the defined lexicon.
x Dependency relation based feature extraction approaches: This approach extracts product features based on
dependency relations among terms appeared in review sentences. Firstly, analyze term dependency relations in
review sentences, then apply some rules and algorithms to extract product features from the identified dependency
relations24.

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3.3. Opinion Classification
Opinion classification (OC) is concerned with identifying the orientation of opinions and classifies them to either
positive or negative. To identify this orientation, we can use a machine learning approach, a lexicon based approach
or a hybrid approach.
x Machine Learning Approach: This approach divided into supervised and unsupervised learning methods. The
supervised learning algorithms need labeled training documents. Contrarily the unsupervised methods are used when
it is difficult to find these labeled training documents25. There are many methods and algorithms supervised
classifiers in OC. The most frequently used classifiers are Support Vector Machine (SVM), Naïve Bayes, Artificial
Neural Network (ANN) and maximum entropy. Many machine learning methods have been investigated for opinion
classification in the literature indicated in table 1. The first column contains the authors of articles, the second
shows the algorithms used and the third illustrates the scope of the data used for evaluating the article’s algorithms.
Table 1: Works on opinion classification using machine learning
Author
Algorithms used
Dataset
Year
A. Mountassir
& H.
Benbrahim26
Nearest Centroid based on Vector Norms
Algorithm (NCVN)
DS1,DS2 and DS3(built, from Aljazeera’s website forums);
Opinion Corpus for Arabic (OCA2); Movie reviews (IMDb)
2014
Changqin Quan
& Fuji Ren27
Kernel methods (KMs)
Product(digital camera, cell phone, mp3 player, and router)
2014
G. Yan et al.28
SVM & N-Gram
Movie reviews
2014
Lin Zh ang29
SVM LibLinear & Naïve Bayes Multinomial
Mobile users
2014
Gang Wang30
Naive Bayes, Maximum Entropy, Decision
Tree, K-NN, & SVM
Digital camera & laptop reviews from Amazon.com; Summer
camp reviews from CampRatingz.com; Reviews of physicians
from RateMDs.com; Reviews of pharmaceutical drugs from
DrugRatingz.com; Reviews of lawyers from
LawyerRatingz.com; Movie, Music, Reviews of radio shows
from RadioRatingz.com; Television show reviews from
TVRatingz.com.
2013
Rodrigo
Moraes31
SVM and Artificial Neural Network (ANN)
Movies review product (GPS, Books and Cameras)
2013
Xue Bai32
Markov Blanket Classifier
Movie reviews (IMDb)
2011
Qiang Ye33
Naïve Bayes, SVM, and N-gram
Travel destinations in the US and Europe
2009
x Lexicon-based Approach: This approach relies on a sentiment lexicon, a collection of known and precompiled
sentiment terms that associate sentiment orientation and words34. They vary significantly, as some sentiment
lexicons define sentiment scores with varying numerical ranges while others define one or more sentiment
categories such as positive, negative, neutral, or a variety of emotions such as joy and sadness.
SentiWordNet is the most frequently used sentiment lexicon to calculate sentiment polarity. This is a tool that
provides the positive, negative and neutral values of nouns, adjectives and verbs. Also, is a lexical resource35 based
on the well-known WordNet. It provides additional information on synsets related to sentiment orientation. A synset
is the basic item of information in WordNet and it represents a concept that is unambiguous. Most of the relations
over the lexical graph use synsets as nodes (hyperonymy, synonymy, homonymy and more). SentiWordNet returns
from every synset a set of three scores representing the notions of positivity, negativity and objectivity (the latter
being computed from the two previous ones). Therefore, every concept in the graph is weighted according to its
subjectivity and polarity. The following table 2 describe the SentiWordNet structure and some sentiment scores
associated to SentiWordNet entries.

364 Kamal Amarouche et al. / Procedia Computer Science 73 ( 2015 ) 358 – 365
Table 2: SentiWordNet structure
POS
Offset
PosScore
NegScore
SynsetTerms
a
01150475
0
0.625
sorry#1
a
02273643
0.5
0
secure#5
a
01838253
0.625
0
fine#2
n
03931044
0
0
image#3
n
03931044
0
0
picture#1
v
01824736
0.125
0
like#1
Such as:
o POS: This can take four possible values: a => adjective; n => noun; v => verb; r => adverb;
o Offset: Numerical ID which associated with part of speech uniquely identifies a synset in the database;
o PosScore: Positive score for this synset. This is a numerical value ranging from 0 to 1;
o NegScore: Negative score for this synset. This is a numerical value ranging from 0 to 1;
o Sysnset Terms: List of all terms included in this synset.
There is much recent work that uses SentiWordNet as a technique of opinion classification: Vibha Soni13 used
this lexical resource to identify the orientation of customer reviews about Samsung Galaxy S5. Kushal Bafna36
relied on this dictionary to organize the reviews according to the polarity of products (Cannon G3 Camera, iPhone
4s, Mp3 player).
x Hybrid Approach: This approach combines both previous approaches. The concept of combining these
approaches is proposed as a new direction for the improvement of the performance of individual classifiers37. In the
opinion mining field, we can notice that there are not many approaches based on hybrid methods. Abd. Samad38
proposes a hybrid method based on Support Vector Machine and Particle Swarm Optimization to classify sentiment
of Movie Review. Vinodhini39 suggests a hybrid machine learning approach built under the framework of
combination of classifiers with principal component analysis.
4. Conclusion
In this paper, we talked about competitive intelligence and its importance after the emergence of available
electronic data sources and its impact. Among these data, we find opinions that come directly from customers and
we justified its importance for competitive intelligence. Finally, a summary on the different methods and works for
phases of product opinion mining was discussed.
The contribution of this paper is double fold: First, it explains the importance of opinions as an information
source for competitive intelligence, precisely in product opinion mining for competitive intelligence; furthermore it
describes its various steps with recent related work. This can help the new comers have a panoramic view on the
entire field.
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