Exploring Public Attitude Towards Children by Leveraging Emoji to Track Out Sentiment Using Distil-BERT a Fine-Tuned Model

Saha, Uchchhwas; Mahmud, Md. Shihab; Keya, Mumenunnessa; Lucky, Effat Ara Easmin; Khushbu, Sharun Akter; Noori, Sheak Rashed Haider; Syed, Muntaser Mansur

doi:10.1007/978-3-031-12413-6_26

Cited by 8 publications

(1 citation statement)

References 30 publications

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“…For example, Saha et al [66], collected data from text posted on social networks and classified how children are harassed by various comments online. The aim of this work was to show society the risks that children face with the use of communication applications.…”

Section: Text Analytics Algorithms On Mobile Devicesmentioning

confidence: 99%

Analysis of Machine Learning Techniques for Information Classification in Mobile Applications

2023

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Due to the daily use of mobile technologies, we live in constant connection with the world through the Internet. Technological innovations in smart devices have allowed us to carry out everyday activities such as communicating, working, studying or using them as a means of entertainment, which has led to smartphones displacing computers as the most important device connected to the Internet today, causing users to demand smarter applications or functionalities that allow them to meet their needs. Artificial intelligence has been a major innovation in information technology that is transforming the way users use smart devices. Using applications that make use of artificial intelligence has revolutionised our lives, from making predictions of possible words based on typing in a text box, to being able to unlock devices through pattern recognition. However, these technologies face problems such as overheating and battery drain due to high resource consumption, low computational capacity, memory limitations, etc. This paper reviews the most important artificial intelligence algorithms for mobile devices, emphasising the challenges and problems that can arise when implementing these technologies in low-resource devices.

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Section: Text Analytics Algorithms On Mobile Devicesmentioning

confidence: 99%