@article {ElAkrouchi2021,
	title = {End-to-end LDA-based automatic weak signal detection in web news},
	journal = {Knowledge-Based Systems},
	volume = {212},
	year = {2021},
	note = {cited By 9},
	abstract = {An extremely competitive business environment requires every company to monitor its competitors and anticipate future opportunities and risks, creating a dire need for competitive intelligence. In response to this need, foresight study became a prominent field, especially the concept of weak signal detection. This research area has been widely studied for its utility, but it is limited by the need of human expert judgments on these signals. Moreover, the increase in the volume of information on the Internet through blogs and web news has made the detection process difficult, which has created a need for automation. Recent studies have attempted topic modeling techniques, specifically latent Dirichlet allocation (LDA), for automating the weak signal detection process; however, these approaches do not cover all parts of the process. In this study, we propose a fully automatic LDA-based weak signal detection method, consisting of two filtering functions: the weakness function aimed at filtering topics, which potentially contains weak signals, and the potential warning function, which helps to extract only early warning signs from the previously filtered topics. We took this approach with a famous daily web news dataset, and we could detect the risk of the COVID19 pandemic at an early stage. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Competition, Competitive business, Competitive intelligence, Detection process, Early warning signs, Filtering functions, Latent dirichlet allocations, Signal detection, Statistics, Topic Modeling, Weak signal detection, Weak signals},
	doi = {10.1016/j.knosys.2020.106650},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097573309\&doi=10.1016\%2fj.knosys.2020.106650\&partnerID=40\&md5=a491fca334a7b047c3a02e16ecb7b3f0},
	author = {El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
@conference {ElAkrouchi2021,
	title = {Review on adopting concept extraction in weak signals detection in competitive intelligence},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2021},
	note = {cited By 0},
	abstract = {The dynamic nature of competition in the business environment makes a company{\textquoteright}s ability to secure future change more critical to its survival. Consequently, efficient exploitation of valuable intel is globally acknowledged as an essential foundation of competitive advantage, leading to Competitive Intelligence. Besides, one of the crucial keys to successful competitive information securing is studying the future. Thus, predicting what may happen in the uncertain future is a leading-edge technology leading to an extensive need for foresight analysis. Foresight study uses various methods to recognize future developments and make plans that anticipate possible future changes. One of the leading techniques used in foresight is detecting and understanding Weak Signals. But knowing the nature of these signals, automatically scanning them is still considered a difficult task. For this, we examine the Concept Extraction technique as a main step to detect weak signals from documents automatically. In this paper, we will explain the concept extraction methods used so far, and we present in detail all the main methods and approaches and their application in detecting weak signals. {\textcopyright} 2021 Association for Computing Machinery.},
	keywords = {Business environments, Competition, Competitive advantage, Competitive intelligence, Concept extraction, Data mining, Dynamic nature, Extraction, Leading edge technology, Possible futures, Signal detection, Topic Modeling, Weak signal detection, Weak signals},
	doi = {10.1145/3485557.3485560},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121505750\&doi=10.1145\%2f3485557.3485560\&partnerID=40\&md5=01161f382ecb38a8faf2883a2bc37003},
	author = {El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
@conference {ElAkrouchi2020,
	title = {Monitoring Early Warning Signs Evolution through Time},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	abstract = {In excessive business competition, detecting weak signals is very important to anticipate future changes and events. The process of detecting weak signals is very challenging, and many techniques were proposed to automatize this challenge but still needs the intervention of experts{\textquoteright} opinion. Understanding those detected signals and their evolution in time is crucial to reveal the alertness of possible future events and warnings. For this reason, this paper proposes a new algorithm to strengthen weak signals into early warning signs. The proposed algorithm aims to monitor and track weak signals{\textquoteright} evolution within time. The output will be a list of early warning signs and visualization to illustrate their evolution in time. Finally, to adequately understand the early warning signs obtained and enhance their semantic alertness, we used Word2Vec modeling to provide semantically similar words to these warning signs and improve their contextual alertness. We tested this algorithm on a web news dataset of 2006-2007 to detect early warning signs related to the 2008 financial crisis ahead of time. We obtained prominent results in strengthening and monitoring the evolution of early warning signs related to this crisis. {\textcopyright} 2020 ACM.},
	keywords = {Artificial intelligence, Business competition, Competition, Early warning signs, Financial crisis, Possible futures, Semantics, Signal detection, Warning signs, Weak signals},
	doi = {10.1145/3446132.3446173},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102970850\&doi=10.1145\%2f3446132.3446173\&partnerID=40\&md5=4f35fa04a194332457fca88ff3bec0d1},
	author = {El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
@conference {Amarouche20162876,
	title = {Introduction to competitive intelligence: Process, applications and tools},
	booktitle = {Proceedings of the 27th International Business Information Management Association Conference - Innovation Management and Education Excellence Vision 2020: From Regional Development Sustainability to Global Economic Growth, IBIMA 2016},
	year = {2016},
	note = {cited By 0},
	pages = {2876-2885},
	abstract = {In the world of excessive business competitiveness, almost every company tries to monitor its environment to exceed the competitors. Getting knowledge about competitors is the basic principal of what is called Competitive Intelligence (CI). Many applications of Competitive Intelligence can be used like Opinion Mining and Foresight studies and the process of obtaining such intelligence differs according to the company{\textquoteright}s needs. In this paper, we will present in more details the definitions of CI and a general process grouping the most used steps in conducting such study. In the end we will present some tools useful in CI. Copyright {\textcopyright} 2016 International Business Information Management Association},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984646253\&partnerID=40\&md5=1c66c097dc1b53d274daa126d36a743f},
	author = {Amarouche, K. and El Akrouchi, M. and Benbrahim, H. and Kassou, I.}
}
