DZchatbot: A Medical Assistant Chatbot in the Algerian Arabic Dialect using Seq2Seq Model

Boulesnane, Abdennour; Saidi, Yaakoub; Kamel, Oussama; Bouhamed, Mohammed Mounir; Mennour, Rostom

doi:10.1109/pais56586.2022.9946867

Cited by 8 publications

(1 citation statement)

References 27 publications

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“…Sentiment analysis, the task of determining the emotional tone of a piece of text, has been widely studied in the field of natural language processing [1][2][3]. However, there has been limited research specifically on the Arabic language with the Algerian dialect [4]. The Algerian dialect, spoken by over 40 million people, is an integral part of the country's cultural identity [5].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis on Algerian Dialect with Transformers

Benmounah,

Boulesnane,

Fadheli

et al. 2023

Applied Sciences

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The task of extracting sentiment from text has been widely studied in the field of natural language processing. However, little work has been conducted specifically on the Arabic language with the Algerian dialect. In this research, we aim to make a significant contribution to the field of sentiment analysis on the Algerian dialect by creating a custom and relatively large dataset with a tailored deep learning model. The dataset was extracted from Algerian YouTube channels and manually annotated by the research team. We then utilize this dataset to train a state-of-the-art deep learning model for natural language processing called BERT, which is a type of Transformer model. Using this model, we were able to achieve an F1-score of 78.38% and an accuracy of 81.74% on the testing set. This demonstrates the effectiveness of our approach and the potential of using BERT for sentiment analysis on the Algerian dialect. Our model can be used to infer sentiment from any Algerian text, thus providing a valuable tool for understanding the opinions and emotions of the population. This research highlights the importance of studying the Algerian dialect and the potential of using state-of-the-art deep learning models for natural language processing in this area.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis on Algerian Dialect with Transformers

Benmounah,

Boulesnane,

Fadheli

et al. 2023

Applied Sciences

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Deep learning for Arabic healthcare: MedicalBot

Abdelhay

Mohammed

Hefny

2023

Soc. Netw. Anal. Min.

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Since the COVID-19 pandemic, healthcare services, particularly remote and automated healthcare consultations, have gained increased attention. Medical bots, which provide medical advice and support, are becoming increasingly popular. They offer numerous benefits, including 24/7 access to medical counseling, reduced appointment wait times by providing quick answers to common questions or concerns, and cost savings associated with fewer visits or tests required for diagnosis and treatment plans. The success of medical bots depends on the quality of their learning, which in turn depends on the appropriate corpus within the domain of interest. Arabic is one of the most commonly used languages for sharing users’ internet content. However, implementing medical bots in Arabic faces several challenges, including the language’s morphological composition, the diversity of dialects, and the need for an appropriate and large enough corpus in the medical domain. To address this gap, this paper introduces the largest Arabic Healthcare Q &A dataset, called MAQA, consisting of over 430,000 questions distributed across 20 medical specializations. Furthermore, this paper adopts three deep learning models, namely LSTM, Bi-LSTM, and Transformers, for experimenting and benchmarking the proposed corpus MAQA. The experimental results demonstrate that the recent Transformer model outperforms the traditional deep learning models, achieving an average cosine similarity of 80.81% and a BLeU score of 58%.

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