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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.
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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
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
Available online at
The International Conference on Advanced Wireless, Information, and Communication
Technologies (AWICT 2015)
Product Opinion Mining for Competitive Intelligence
ALBIRONI Research Team, ENSIAS, Mohammed 5 University, Rabat, Morocco
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
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
Peer-review under responsibility of organizing committee of the International Conference on Advanced Wireless, Information,
and Communication Technologies (AWICT 2015)
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.
Kamal Amarouche et al. / Procedia Computer Science 73 ( 2015 ) 358 – 365
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
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
Kamal Amarouche et al. / Procedia Computer Science 73 ( 2015 ) 358 – 365
Algorithms used
A. Mountassir
& H.
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)
Changqin Quan
& Fuji Ren27
Kernel methods (KMs)
Product(digital camera, cell phone, mp3 player, and router)
G. Yan et al.28
SVM & N-Gram
Movie reviews
Lin Zh ang29
SVM LibLinear & Naïve Bayes Multinomial
Mobile users
Gang Wang30
Naive Bayes, Maximum Entropy, Decision
Tree, K-NN, & SVM
Digital camera & laptop reviews from; Summer
camp reviews from; Reviews of physicians
from; Reviews of pharmaceutical drugs from; Reviews of lawyers from; Movie, Music, Reviews of radio shows
from; Television show reviews from
SVM and Artificial Neural Network (ANN)
Movies review product (GPS, Books and Cameras)
Xue Bai32
Markov Blanket Classifier
Movie reviews (IMDb)
Qiang Ye33
Naïve Bayes, SVM, and N-gram
Travel destinations in the US and Europe
364 Kamal Amarouche et al. / Procedia Computer Science 73 ( 2015 ) 358 – 365
Table 2: SentiWordNet structure
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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Shopee merupakan salah satu e-commerce model e-commerce Customer-To Customer (C2C) menggunakan aplikasi sebagai tempat untuk melakukan bisnis. Shopee memberikan beberapa pilihan akses kepada customer diantaranya melalui website dan mobile platform demi kemudahan kegiatan berbelanja online. Untuk mencari informasi tentang peluang yang dapat ditingkatkan dari layanan yang ada Shopee dapat memanfaatkan review dari customer untuk mengetahui masalah yang terjadi pada layanan yang diberikan kepada customer. Review dari customer e-commerce berbasis aplikasi tersebut dapat menjadi solusi yang lebih menjelaskan apa yang disukai dan tidak disukai customer sehingga bisa digunakan sebagai alat untuk menilai aspek-aspek pada layanan e-commerce yang dinilai bermasalah. Untuk mencari informasi tentang peluang yang dapat ditingkatkan pada layanan e-commerce dari komplain atau keluhan review customer. dilakukan dengan mengklasifikasi review kedalam beberapa aspek dengan menggunakan Text Classification untuk mengklasifikasikan aspek. Dari hasil tersebut didapat lah rangkuman masalah berdasarkan aspek yang bisa diperbaiki oleh pihak e-commerce untuk tetap meningkatkan layanan.
... Furthermore, Natural Language Processing (NLP) methods, including text mining, are being used to understand many parts of the business landscape including customer needs, product competitiveness, and company performance. Specifically, researchers have surveyed the area of competitive intelligence for products and have demonstrated the promise of approaches using NLP and text mining (Amarouche, Benbrahim, and Kassou 2015). ...
Competitive analysis is a critical part of any business. Product managers, sellers, and marketers spend time and resources scouring through an immense amount of online and offline content, aiming to discover what their competitors are doing in the marketplace to understand what type of threat they pose to their business’ financial well-being. Currently, this process is time and labor-intensive, slow and costly. This paper presents Clarity, a data-driven unsupervised system for assessment of products, which is currently in deployment in the global technology company, IBM. Clarity has been running for more than a year and is used by over 4,500 people to perform over 200 competitive analyses involving over 1000 products. The system considers multiple factors from a collection of online content: numeric ratings by online users, sentiment of user generated online content for key product performance dimensions, content volume, and topic analysis of content. The results and explanations of factors leading to the results are visualized in an interactive dashboard that allows users to track their product’s performance as well as understand main contributing factors. Its efficacy has been tested in a series of cases across IBM’s portfolio which spans software, hardware, and services. After initial release and first year of use, improvements to the methodology were implemented to ensure it was relevant to and served the highest impact needs of target users. Moreover, new use cases leveraging the initial ideas and approaches continue to be explored.
... The available opinion mining systems (Vinodhini and Chandrasekaran 2016;Ahmad and Doja 2013;Amarouche et al. 2015;Quan and Ren 2014;Tsirakis et al. 2017;Sadhana et al. 2017) are mining customer reviews without considering the manufacturer's perspective. Customers are heavily reliant on peer viewpoints when they purchase a new product and product information is essential to support the recommendation. ...
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With the increasing number of customer reviews on the Web, there is a growing need for effective methods to retrieve valuable information hidden in these reviews, as sellers need to gain a deep understanding of customers’ preferences in a timely manner. With the con-tinuous enhancement of opinion mining or sentiment analysis research, researchers have proposed many automatic mining and classification methods. However, how to choose a trusted method is a difficult problem for companies, because customer reviews (or opin-ions) contain a lot of uncertain information and noise. This article reports on a detailed survey of recent opinion mining literature. It also reviews how to extract text features in opinions that may contain noise or uncertainties, how to express knowledge in opinions, and how to classify them. Through this extensive study, this paper discusses open ques-tions and recommends future research directions for building the next generation of opin-ion mining systems. (PDF) Mining and Summarizing Customer Reviews : Survey. Available from: [accessed Dec 01 2021].
... Investigaciones como las de (Aakash & Aggarwal, 2020;Amarouche et al., 2015;Nguyen et al., 2019) presentan algunos estudios relacionados a plataformas digitales y que son de utilidad para el análisis de mercado, es el caso de la detección de Hot Topics y Análisis de sentimientos. Para alimentar los modelos se realiza una extracción de datos a través de las API de las diferentes plataformas; sin embargo, estos estudios no se enfocan en la extracción de datos, sino en otros tipos de análisis. ...
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En la actualidad, en especial en tiempos de pandemia, el comercio electrónico se convierte en la manera predominante de comercializar productos y servicios en el mundo. Un estudio realizado por la Cámara Ecuatoriana de Comercio Electrónico, en el año 2020, demuestra que las compras y ventas a través de canales digitales se han incrementado al menos 15 veces desde el inicio de la pandemia. Por lo tanto, para realizar estudios de mercado las empresas deben buscar nuevas formas de extraer información para luego desarrollar un análisis de la misma y así obtener una ventaja competitiva. La extracción de datos es un proceso complejo y poco escalable, de manera que, esta investigación presenta una metodología para la extracción de información de un sector industrial determinado. La metodología consiste en dos pasos fundamentales, primero se realiza un ranking de las principales fuentes de información disponibles y más utilizadas en un determinado sector de la industria, se consideran varias características y opinión de expertos. Segundo, se propone una plataforma, la cual integra las fuentes de información mejor rankeadas y realiza la extracción de datos. Finalmente, estos datos se presentan en un Dashboard con la disponibilidad de poder descargar y hacer uso en estudios posteriores. Se concluye que las 4 plataformas que mayor beneficio ofrecen para esta investigación son: Google Trends, Facebook, YouTube y Twitter. También existen fuentes de información que al aplicar el análisis propuesto tienen una calificación alta, sin embargo, la extracción de los datos es difícil debido a sus políticas de seguridad.
Recent developments in information technology generating massive amount of data are referred to as big data. Such data with variety and velocity pose a challenge to supply chain management (SCM) practitioners on how to deal with them to draw valued insights for enhanced decision-making. The analysis of big data can offer unique intuitions into supply and market dynamics like understanding the customer preferences, developing new products, demand forecasting, supplier selection and evaluation, process improvements, quality control, capacity planning, managing delivery schedules, order management, etc., to reduce the supply chain costs and improve product availability. Thus, this chapter reviews and classifies the literature on big data analytics (BDA) application in SCM. We extracted and reviewed about 200 academic journal and conference articles from 2010 to 2017 from various research databases to know the extent of BDA applications in different supply chain domains (plan, source, make, deliver and return). The papers were also classified based on analytics (descriptive, predictive and prescriptive) and the supply chain resources utilized (financial, human, technological, organizational and intangibles). Based on the review results, we propose a supply chain (SC) visibility framework that identifies SC visibility as a key driving force for SC transformation, achieved through strong BDA capability. The findings of this review and future research directions will help the academics, researchers and practitioners to focus on the BDA opportunities and challenges.
Robo-advisors (RAs) support economic decisions for customers using artificial intelligence (AI). RAs are gaining increasing significance but lack market penetration. A significant issue is the perceived low transparency of such AI systems. This study examines the public’s demands on RAs with text-mining methods from the perspective of explainable artificial intelligence (XAI). The results reveal understandability and trustworthiness issues for each of the RA use phases (configuration, matching, and maintenance). In particular, five barriers emerge in RA if information needs remain unanswered: entry barrier, assessment barrier, evaluation barrier, continuation barrier and withdrawal barrier. The barriers can be mitigated by combining explanation, design and communication measures. The results are discussed regarding theoretical implications and practical recommendations for facilitating the adoption of RAs. JEL: D8 (D81, D83, D89), G1 (G11), G2 (G20, G23), G4 (G41), I2 (I24, I25), O3 (O31, O33)
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With regard to today's social media networks, memes have become central character where millions of memes are shared per second on different social media networks. The detection of memes is a very concentrated and demanding subject in the current era. Today's social media (What's App, Twitter, and Facebook) is widespread around the world. People in all countries use these networks and spend their plentiful time on daily basis. As social media has an enormous amount of data overall in the world. Meme detection from media networks can be done by using their authenticated APIs. For this analysis we used some opinion mining techniques and sentiment analysis like statistical descriptive and content analysis. In our society, it is the better way to analyze about any journalist because social media can provide very huge amounts of data about any journalist however the authenticity is compromised, what is true or false, no one bother to check. Anyone can make approximate correct perceptions by using sentiment analysis and text mining techniques. It will provide highly wanted and hidden characteristics and perceptions for searchers and demanding people about journalists. Finally use for sentiment analysis by using Python.
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Teknolojinin hayatımıza büyük bir katkı sağladığı ve toplum üzerinde önemli bir etkiye sahip olduğu bir gerçektir. Teknolojinin gelişmesiyle birlikte internette üretilen veri miktarı da artmıştır. Özellikle sosyal medya, kullanıcıların görüş ve duygularını rahatlıkla ifade edebildiği önemli bir mecraya dönüşmüştür. Bu noktada, birçok alanda kullanıcıların internette paylaştıkları duygu ve düşünceleri Doğal Dil İşleme (DDİ)'nin alanlarından biri olan "fikir madenciliği" yöntemiyle incelenmekte ve kullanıcıların duygu analizi yapılmaktadır. Bu çalışmanın amacı da toplum çevirmenlerinin duygu ve düşüncelerinin fikir madenciliği yöntemiyle incelenebileceğine dair bir yaklaşım sunmaktır. Bir diğer deyişle, daha çok bilgisayar bilimleri ve yönetim bilişim sistemleri disiplinlerinde kullanılan fikir madenciliği ve duygu analizi yöntemlerini tanıtarak, toplum çevirmenliği araştırmalarına yeni bir bakış açısı sunmak hedeflenmiştir. Bu amaçla, fikir madenciliği ile duygu analizi yönteminin toplum çevirmenliği araştırmalarına katkıları betimleyici bir yaklaşımla tartışılmaktadır.
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The purpose of this paper is to assess the impact of competitive intelligence on the competitive advantage of corporate organizations. While much empirical works have centered on competitive advantage, the generalization of its relationship to competitive intelligence in the Iran context has been under researched. A 32-item survey questionnaire to measure competitive intelligence and competitive advantage was developed and corporates in Iran are selected from industrial estates companies in the kerman city as a sample for this study. For analysis data used of the SPSS 16 and appraisal of model by Amos graphics 18. The results of the study reported in this paper validated and finds strong association between competitive intelligence and competitive advantage of corporate organizations in the Iran context. The main finding of this study is that competitive intelligence lead to competitive advantage in corporate organizations in Iran. The implications of the results of this study are clear for scholars and managers.
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In recent years, the dramatic increase of smartphone and tablet applications has allowed users to comment on various service platforms at any time through mobile internet, social media, cloud computing, and etc. While unfortunately, up to now, very few studies of classification methods have been applied in such area. In this paper, we concluded the following unique characteristics through more than 1,400,000 real mobile application reviews: (1) Short average length; (2) Large span of length; (3) Power-law distribution and (4) Significant difference in polarity. Based on above mentioned characteristics, a series of comparative experiments have been done for emotion classifications through classification algorithms, feature representations and review length, respectively.
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Online consumer product reviews are a main source for consumers to obtain product information and reduce product uncertainty before making a purchase decision. However, the great volume of product reviews makes it tedious and ineffective for consumers to peruse individual reviews one by one and search for comments on specific product features of their interest. This study proposes a novel method called EXPRS that integrates an extended PageRank algorithm, synonym expansion, and implicit feature inference to extract product features automatically. The empirical evaluation using consumer reviews on three different products shows that EXPRS is more effective than two baseline methods.
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Background: Competitive intelligence (CI) provides actionable intelligence, which provides a competitive edge in enterprises. However, without proper process, it is difficult to develop actionable intelligence. There are disagreements about how the CI process should be structured. For CI professionals to focus on producing actionable intelligence, and to do so with simplicity, they need a common CI process model.Objectives: The purpose of this research is to review the current literature on CI, to look at the aims of identifying and analysing CI process models, and finally to propose a universal CI process model.Method: The study was qualitative in nature and content analysis was conducted on all identified sources establishing and analysing CI process models. To identify relevant literature, academic databases and search engines were used. Moreover, a review of references in related studies led to more relevant sources, the references of which were further reviewed and analysed. To ensure reliability, only peer-reviewed articles were used.Results: The findings reveal that the majority of scholars view the CI process as a cycle of interrelated phases. The output of one phase is the input of the next phase.Conclusion: The CI process is a cycle of interrelated phases. The output of one phase is the input of the next phase. These phases are influenced by the following factors: decision makers, process and structure, organisational awareness and culture, and feedback.
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With the growing availability and popularity of opinion-rich resources such as review forums for the product sold online, choosing the right product from a large number of products have become difficult for the user. For trendy product, the number of customers’ opinions available can be in the thousands. It becomes hard for the customers to read all the reviews and if he reads only a few of those reviews, then he may get a biased view about the product. Makers of the products may also feel difficult to maintain, keep track and understand the customers’ views for the products. Several research works have been proposed in the past to address these issues, but they have certain limitations: The systems implemented are completely opaque, the reviews are not easier to perceive and are time consuming to analyze because of large irrelevant information apart from actual opinions about the features, the feature based summarization system that are implemented are more generic ones and static in nature. In this research, we proposed a dynamic system for feature based summarization of customers’ opinions for online products, which works according to the domain of the product. We are extracting online reviews for a product on periodic bases, each time after extraction, we carry out the following work: Firstly, identification of features of a product from customers’ opinions is done. Next, for each feature, its corresponding opinions’ are extracted and their orientation or polarity (positive/negative) is detected. The final polarity of feature-opinions pairs is calculated. At last, feature based summarizations of the reviews are generated, by extracting the relevant excerpts with respect to each feature-opinions pair and placing it into their respective feature based cluster. These feature based excerpts can easily be digested by the user.
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The importance of competitive intelligence in companies is practically become widely accepted. Use of this tool has become a necessity of today. But the need for the introduction of a comprehensive (complex) competitive intelligence is penetrating into the foreground on the basis of the most important proven benefits to the enterprise, such as improved quality of information, faster decision making, systematic improvement of organizational processes, improvement of organizational efficiency, cost reduction, improvement of information dissemination, saving time, quicker identification of threats and opportunities. The main aim of this article is to assess the importance of competitive intelligence based on various surveys and especially to assess its complex integration throughout the entire enterprise, not just in the marketing departments.
Conference Paper
In this paper, we present a novel model that we propose for document representation. In contrast with the classical Vector Space Model which represents each document by a unique vector in the feature space, our model consists in representing each document by a vector in the space of training documents of each category. We develop, for this novel model, a discriminative classifier which is based on the norms of the generated vectors by our model. We call this algorithm the Nearest Cetroid based on Vector Norms. Our major goal, by the proposition of such new classification framework, is to overcome the problems related to huge dimensionality and vector sparsity which are commonly faced in Text Classification problems. We evaluate the performance of the proposed framework by comparing its effectiveness and efficiency with those of some standard classifiers when used with the classical document representation. The studied classifiers are Naïve Bayes (NB), Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). We conduct our experiments on multi-lingual balanced and unbalanced binary data sets. Our results show that our algorithm typically performs well since it is competitive with the classical methods and, at the same time, dramatically faster especially in comparison with NB and kNN. We also apply our model on the Reuters21578 corpus so as to evaluate its performance in a multi-class environment. We can say that the obtained result (85.4% in terms of micro-F1) is promising and that it can be improved in future works.
Opinion mining is a discipline or area of text classification which continues gives contribution in research field. Sentiment analysis is one another name of Opinion mining. Opinion Mining analyse and classify the user generated data like reviews, blogs, comments, articles etc. Nowadays every people use web services and gives their opinions about every field, domain or peoples. The main objective of Opinion mining is Sentiment Classification i.e. to classify the opinion into positive or negative classes. There are basically two approaches first machine learning Or Supervised learning techniques and other unsupervised learning techniques. In this paper an unsupervised lexicon technique is used for Sentiment Classification.
While multimedia digital documents are progressively spreading, most of the content of Digital Libraries is still in the form of text, and this predominance will probably never be questioned. Except pure display of these documents, all other tasks are based on some kind of Natural Language Processing, that must be supported by suitable linguistic resources. Since these resources are clearly language-specific, they might be unavailable for several languages, and manually building them is costly, time-consuming and error-prone. This paper proposes a methodology to automatically learn linguistic resources for a natural language starting from texts written in that language. The learned resources may enable further high-level processing of documents in that language, and/or be taken as a basis for further manual refinements. Experimental results show that its application may effectively provide useful linguistic resources in a fully automatic manner.