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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Omar Bencharef
Cadi Ayyad University
Computer Science & IT Engineering

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The impact of influencers on the companies reputation in developing countries: Case of Morocco Mohamed Chiny; Marouane Chihab; El Mahdi Juiher; Khaoula Jabari; Omar Bencharef; Younes Chihab
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp410-419

Abstract

With the emergence of social networks and their adoption by a large number of users, the importance of influencers continues to grow and companies are in a frantic race to recruit those most likely to promote their reputation and brand image. However, in the existing literature, there is little work that conducts quantitative studies on this subject in developing countries. For this reason, we conducted a study that attempts to understand the importance of influencers in reshaping public opinion of a company or brand. We chose as a subject of study a large Moroccan company operating in the telecommunications sector that hired a popular influencer among young Moroccans. We then adopted an approach based on scraping and analyzing the occurrences of the influencer's posts on Instagram and the content of the company's website and then publishing a questionnaire to 180 respondents in the age range of most of the followers of the influencer in question. The results suggest that a positive relationship exists between the influencer and brand reputation, meaning that if the person is following the influencer who has published content on the brand, that person is expected to be systematically aware of the brand, and vice versa.
Effect of word embedding vector dimensionality on sentiment analysis through short and long texts Mohamed Chiny; Marouane Chihab; Abdelkarim Ait Lahcen; Omar Bencharef; Younes Chihab
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp823-830

Abstract

Word embedding has become the most popular method of lexical description in a given context in the natural language processing domain, especially through the word to vector (Word2Vec) and global vectors (GloVe) implementations. Since GloVe is a pre-trained model that provides access to word mapping vectors on many dimensionalities, a large number of applications rely on its prowess, especially in the field of sentiment analysis. However, in the literature, we found that in many cases, GloVe is implemented with arbitrary dimensionalities (often 300d) regardless of the length of the text to be analyzed. In this work, we conducted a study that identifies the effect of the dimensionality of word embedding mapping vectors on short and long texts in a sentiment analysis context. The results suggest that as the dimensionality of the vectors increases, the performance metrics of the model also increase for long texts. In contrast, for short texts, we recorded a threshold at which dimensionality does not matter.
Hybrid Forex prediction model using multiple regression, simulated annealing, reinforcement learning and technical analysis Hana Jamali; Younes Chihab; Iván García-Magariño; Omar Bencharef
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp892-911

Abstract

Foreign exchange market refers to the market in which currencies from around the world are traded. It allows investors to buy or sell a currency of their choice. Forex interests several categories of stakeholders, such as companies that carry out international contracts, large institutional investors, via the main banks, which carry out transactions on this market for speculative purposes. One of the most important aspects in the Forex market is knowing when to invest by buying, selling, and this through the recorded trend of a currency pair, but given the characteristics of the Forex market namely its chaotic, noisy and not stationary nature, prediction becomes a big challenge for traders when it comes to predicting accuracy. This paper aims to predict the right action to be taken at a certain moment through the development of a model that combines multiple techniques such multiple regression, simulated annealing meta-heuristics, reinforcement learning and technical indicators.
Co-Authors Abdelkarim Ait Lahcen El Mahdi Juiher Hana Jamali Iván García-Magariño Khaoula Jabari Marouane Chihab Mohamed Chiny Younes Chihab