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Sentiment analysis is an essential task in natural language processing that involves identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence refers to developing artificial intelligence models and systems that can provide clear, understandable, and transparent explanations for their decisions and predictions. In deep learning, where complex neural networks often operate as "black boxes", the importance of explainable AI lies in enhancing trust, accountability, and interoperability. For further advancement of explainable artificial intelligence in deep learning, gaining a deep understanding of its applications, approaches, evaluation metrics, current advancements, and challenges is imperative. Therefore, in this article, we began exploring the vast array of applications of explainable AI in different deep learning models, scrutinizing them within the context of existing research. We then explored explainable AI approaches used in Deep Learning models and discussed prevalent evaluation metrics used in evaluating a model's explainability. Subsequently, we precisely reviewed the experimental results and advancements of recent state-of-the-art experiments related to explainable AI in deep learning. Finally, we discussed the diverse challenges encountered in sentiment analysis and proposed future research directions to mitigate these concerns. This extensive review provides a complete understanding of explainable AI in deep learning, covering its applications, approaches, experimental analysis, challenges, and research directions. [ABSTRACT FROM AUTHOR]