Ontology-Based Healthcare Hierarchy Towards Chatbot

Adhikary, Prottay Kumar; Manna, Riyanka; Laskar, Sahinur Rahman

doi:10.1007/978-3-031-10766-5_26

Cited by 2 publications

(1 citation statement)

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“…Moreover, processing natural language is a challenging problem due to the significance and meaning of the words used in each situation (the same word has many definitions). To study this problem, the most appropriate solution is to use AI solutions such as ontologies, semantic-based solutions, and others [11], [12]. However, these methods are not good enough to respond to the client's requirements quickly and make an accurate response due to the complexity of the treatment.…”

Section: Introductionmentioning

confidence: 99%

Empowering customer satisfaction chatbot using deep learning and sentiment analysis

Merizig,

Belouaar,

Mghezzi Bakhouche

et al. 2024

Bulletin EEI

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The rapid advancement of technology holds great promise for various types of users, clients, or service providers. Intelligent robots, whether virtual or physical, can simplify the reservation process. With the development of machines and processing tools, natural language processing (NLP) and natural language understanding (NLU) have emerged to help people comprehend spoken language through machines. In order to facilitate seamless human-machine interaction, we aim to address customer needs through a chatbot. The objective of this paper is to incorporate sentiment analysis techniques with deep learning algorithms to cater to customers’ needs during message exchanges. This study aims to create an intelligent chatbot to engage customers during their routine operations and offer support. In addition, it offers to companies a manner to detect sarcastic messages. The proposed chatbot utilizes deep learning techniques to predict users’ intentions based on the questions asked and provide a helpful and convenient answer. A new chatbot for the customer is presented to overcome with challenges related to a wrong statement like sarcastic one and feedback towards user messages. A comparison between deep and transfer learning gives a new insight to include sentiments and sarcasm detection in the conversion process.

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Section: Introductionmentioning

confidence: 99%