Sentiment Analysis of English Texts Using Deep Learning Methods: Comparative Study

Qararia, Aseel, Jazzar, Mahmmoud, Eleyan, Amna ORCID: https://orcid.org/0000-0002-2025-3027 and Bejaoui, Tarek (2025) Sentiment Analysis of English Texts Using Deep Learning Methods: Comparative Study. In: 2025 International Conference on Smart Applications, Communications and Networking (SmartNets), pp. 1-6. Presented at International Conference on Smart Applications, Communications and Networking (SmartNets), 22 - 24 July 2025, Istanbul, Turkiye.

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Abstract

With the great development of social media and people's increasing reliance on it, these platforms produce huge amounts of data daily, which reflect users' opinions and interactions in various fields. In this research, we chose Twitter for the study, as it is one of the most prominent platforms that allow opinions to be freely and directly expressed. Due to the huge volume of data published on Twitter, analyzing and understanding it becomes a difficult task without relying on advanced techniques such as deep learning (DL). Therefore, this study came to compare several popular methods in analyzing tweet sentiment while evaluating their performance using different criteria: accuracy, error rate, precision, recall, F1 score, and training time. In this study, we used the most common methods in sentiment analysis, namely Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). The results showed that BERT is the best method for this task, achieving 91% accuracy and a 9% error rate, but its training time was much longer compared to other methods. Although BERT is the best choice, RNN or CNN are better alternatives when speed is a priority and resources are limited.