Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Fatima Es-Sabery"'
Autor:
Fatima Es-Sabery, Ibrahim Es-Sabery, Abdellatif Hair, Beatriz Sainz-De-Abajo, Begonya Garcia-Zapirain
Publikováno v:
IEEE Access, Vol 10, Pp 87870-87899 (2022)
Emotion processing has been a very intense domain of investigation in data analysis and NLP during the previous few years. Currently, the algorithms of the deep neural networks have been applied for opinion mining tasks with good results. Among vario
Externí odkaz:
https://doaj.org/article/6aa58cbada9546fcacc1a52a91e08583
Autor:
Fatima Es-Sabery, Abdellatif Hair, Junaid Qadir, Beatriz Sainz-De-Abajo, Begona Garcia-Zapirain, Isabel Torre-Diez
Publikováno v:
IEEE Access, Vol 9, Pp 17943-17985 (2021)
At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and thei
Externí odkaz:
https://doaj.org/article/9aa6fd86dd734647b01239f212e93c59
Autor:
Fatima Es-Sabery, Khadija Es-Sabery, Junaid Qadir, Beatriz Sainz-De-Abajo, Abdellatif Hair, Begona Garcia-Zapirain, Isabel De La Torre-Diez
Publikováno v:
IEEE Access, Vol 9, Pp 58706-58739 (2021)
Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social m
Externí odkaz:
https://doaj.org/article/aaaef0f196d841408c4f036ca2453eec
Autor:
Driss Ait Omar, Hamid Garmani, Fatima Es-Sabery, Mohamed El Amrani, Es-Said Azougaghe, Mohamed Baslam
Publikováno v:
Journal of Computer Networks and Communications, Vol 2022 (2022)
This paper tries to investigate the complex characteristics of the communication market where Internet service providers (ISP) lease network access services and compete to serve a large pool of subscribers. For this purpose, we analyze the dynamics o
Externí odkaz:
https://doaj.org/article/6bb9e44a1644417096d71a5bd9534d00
Publikováno v:
Infocommunications journal. 14:85-96
Sentiment analysis is the process of recognizing and categorizing the emotions being expressed in a textual source. Tweets are commonly used to generate a large amount of sentiment data after they are analyzed. These feelings data help to learn about
Autor:
Junaid Qadir, Isabel de la Torre-Díez, Beatriz Sainz-De-Abajo, Abdellatif Hair, Fatima Es-sabery, Begona Garcia-Zapirain
Publikováno v:
IEEE Access, Vol 9, Pp 17943-17985 (2021)
At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and thei
Publikováno v:
Business Intelligence ISBN: 9783031064579
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3a075ca7772d10fbdd1d1a5b77b420b2
https://doi.org/10.1007/978-3-031-06458-6_1
https://doi.org/10.1007/978-3-031-06458-6_1
Autor:
Abdellatif Hair, Fatima Es-sabery
Publikováno v:
Fuzzy Information and Engineering. 11:446-473
Decision tree is the most efficient and fast technology of data mining that is frequently used in data analysis and prediction. According to the development in science and technology in the last ye...
Publikováno v:
E3S Web of Conferences, Vol 297, p 01052 (2021)
This contribution proposes a new model for sentiment analysis, which combines the convolutional neural network (CNN), C4.5 decision tree algorithm, and Fuzzy Rule-Based System (FRBS). Our suggested method consists of six parts. Firstly we have applie
Publikováno v:
Business Intelligence ISBN: 9783030765071
CBI
CBI
In this contribution, we introduce an innovative classification approach for opinion mining. We have used the feature extractor Fast Text to detect and capture the given tweets’ relevant data efficiently. Then, we have applied the feature selector
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1fd7e1c7b5308c091249275c150ff049
https://doi.org/10.1007/978-3-030-76508-8_13
https://doi.org/10.1007/978-3-030-76508-8_13