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      A hybrid neural network model based on transfer learning for Arabic sentiment analysis of customer satisfaction

      1 , 2 , 3
      Engineering Reports
      Wiley

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          Abstract

          Sentiment analysis, a method used to classify textual content into positive, negative, or neutral sentiments, is commonly applied to data from social media platforms. Arabic, an official language of the United Nations, presents unique challenges for sentiment analysis due to its complex morphology and dialectal diversity. Compared to English, research on Arabic sentiment analysis is relatively scarce. Transfer learning, which applies the knowledge learned from one domain to another, can address the limitations of training time and computational resources. However, the development of transfer learning for Arabic sentiment analysis is still underdeveloped. In this study, we develop a new hybrid model, RNN‐BiLSTM, which merges recurrent neural networks (RNN) and bidirectional long short‐term memory (BiLSTM) networks. We used Arabic bidirectional encoder representations from transformers (AraBERT), a state‐of‐the‐art Arabic language pre‐trained transformer‐based model, to generate word‐embedding vectors. The RNN‐BiLSTM model integrates the strengths of RNN and BiLSTM, including the ability to learn sequential dependencies and bidirectional context. We trained the RNN‐BiLSTM model on the source domain, specifically the Arabic reviews dataset (ARD). The RNN‐BiLSTM model outperforms the RNN and BiLSTM models with default parameters, achieving an accuracy of 95.75%. We further applied transfer learning to the RNN‐BiLSTM model by fine‐tuning its parameters using random search. We compared the performance of the fine‐tuned RNN‐BiLSTM model with the RNN and BiLSTM models on two target domain datasets: ASTD and Aracust. The results showed that the fine‐tuned RNN‐BiLSTM model is more effective for transfer learning, achieving an accuracy of 95.44% and 96.19% on the ASTD and Aracust datasets, respectively.

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          Long Short-Term Memory

          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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            Glove: Global Vectors for Word Representation

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              Bidirectional recurrent neural networks

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Engineering Reports
                Engineering Reports
                Wiley
                2577-8196
                2577-8196
                October 2024
                March 03 2024
                October 2024
                : 6
                : 10
                Affiliations
                [1 ] Department of Mathematics Pan African University, Institute for Basic Sciences, Technology and Innovation Nairobi Kenya
                [2 ] School of Computing and Information Technology, Department of Computing Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya
                [3 ] Statistics and Actuarial Sciences Department Dedan Kimathi University of Technology Nyeri Kenya
                Article
                10.1002/eng2.12874
                d95468c6-dcb0-4572-a47b-dc0b8a0daea6
                © 2024

                http://creativecommons.org/licenses/by/4.0/

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