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Title: Sentiment Analysis of Multilingual Dataset of Bahraini Dialects, Arabic, and English
Authors: Omran, T
Sharef, B
Grosan, C
Li, Y
Keywords: Bahraini dialects resources;Bahraini resources scarcity;deep learning;products reviews
Issue Date: 30-Mar-2023
Publisher: MDPI
Citation: Omran, T. et al. (2023) 'Sentiment Analysis of Multilingual Dataset of Bahraini Dialects, Arabic, and English', Data, 8 (4), 68, pp. 1 - 13. doi: 10.3390/data8040068.
Abstract: Copyright © 2023 by the authors. Sentiment analysis is an application of natural language processing (NLP) that requires a machine learning algorithm and a dataset. In some cases, the dataset availability is scarce, particularly with Arabic dialects, precisely the Bahraini ones, which necessitates using an approach such as translation, where a rich source language is exploited to create the target language dataset. In this study, a dataset of Amazon product reviews in Bahraini dialects is presented. This dataset was generated using two cascading stages of translation—a machine translation followed by a manual one. Machine translation was applied using Google Translate to translate English Amazon product reviews into Standard Arabic. In contrast, the manual approach was applied to translate the resulting Arabic reviews into Bahraini ones by qualified native speakers utilizing constructed customized forms. The resulting parallel dataset of English, Standard Arabic, and Bahraini dialects is called English_Modern Standard Arabic_Bahraini Dialects product reviews for sentiment analysis “E_MSA_BDs-PR-SA”. The dataset is balanced, composed of 2500 positive and 2500 negative reviews. The sentiment analysis process was implemented using a stacked LSTM deep learning model. The Bahraini dialect product dataset can be utilized in the transfer learning process for sentimentally analyzing another dataset in Bahraini dialects.
Description: Data Availability Statement: The dataset is openly available at: (accessed on 15 February 2023). Dataset: Dataset License: CC-BY-NC.
Other Identifiers: ORCID iD: Thuraya Omran; Crina Grosan; Yongmin Li
Appears in Collections:Dept of Computer Science Research Papers

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