## 5 Conclusions and Future Work  
The selection of language, feature extraction techniques, and classification algorithms plays an important role in building robust predictive models for sentiment analysis. In this study, we explore the relationships between these three elements and demonstrate the effectiveness of various combinations. The work is situated within the domain of business intelligence, with a specific focus on the tourism sector, particularly hotel reviews as a practical case study. We focus on the three primary languages used in Morocco, English, French, and Arabic, to target a broad multilingual population. The data were vectorized using multiple word representation techniques, including TF-IDF, Word2 Vec, FastText, and BERT embeddings. To resolve class imbalance, the datasets were balanced using the SMOTE technique. These representations were then combined with various machine learning and deep learning classifiers. The results were evaluated using multiple performance metrics such as accuracy, F1-score, precision, recall, ROC AUC curve, and a novel metric evaluating the execution time required to generate word representations. TF-IDF paired with ensemble classifiers consistently outperformed other models across all three datasets. BERT embeddings combined with hybrid classifiers, specifically BiLSTM and CNN, achieved high performance on French and Arabic datasets. Word2 Vec and FastText showed strong results on the English dataset when used with BiLSTM algorithms; in contrast, Word2 Vec outperformed FastText in all experimental settings. These findings provide useful insights for choosing the optimal combination of feature extraction techniques and classification algorithms to develop effective sentiment analysis models. Looking ahead, we plan to expand our research by incorporating other widely used languages in our region, such as Standard Moroccan Amazigh and Spanish. We aim to experiment with alternative hybrid feature extraction techniques. Furthermore, we intend to deploy these models in real-time applications and develop a visual platform for analyzing customer feedback instantaneously. This advancement will not only enrich academic discussions in the field of natural language processing but also enhance the practical application of these technologies in business contexts, where understanding consumer sentiment is essential for making decisions. Furthermore, to ensure fair cross-language evaluations in future work, we propose using standardized multilingual embeddings, such as multilingual BERT, language-agnostic sentence representations (LASER), or multilingual unsupervised and supervised embeddings (MUSE), or training word embeddings on a unified multilingual corpus. This would allow better isolation of language-specific effects and min imize bias introduced by differences in embedding quality. Additionally, benchmarking with expert-annotated sentiment datasets could help validate models' performance against a more objective labeling standard. H.J. : conceptualization, methodology, software, writing\(-\)review and editing, writing\(-\)original draft, visualization, validation, preparation of datasets, data curation. S.E.H. : writing\(-\)review and editing, validation, formal analysis, conceptualization, supervision, and checking the revisions. M.-A.E.H. : visualization, methodology, data curation, and checking the revisions. S.A.: visualization, formal analysis, data curation. A.H.: visualization and validation. All authors have read and agreed to the published version of the manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The English dataset used in this study is publicly available via Hugging Face at ([[https://huggingface.co/datasets/ashraq/hotel-reviews](https://huggingface.co/datasets/ashraq/hotel-reviews)]([https://huggingface.co/datasets/ashraq/hotel-reviews](https://huggingface.co/datasets/ashraq/hotel-reviews)), accessed on 25 October 2024).