## 4 Results and Discussion
### Experimental Findings

Table 10: A comparison of DL algorithm performance using BERT embedding. Figure 9: A comparison of model performance across different vectorization techniques on the English and French datasets. Figure 11: The ROC AUC scores for all feature extraction techniques applied to the Arabic and French datasets. Figure 12: The ROC AUC scores for all feature extraction techniques applied to the English dataset. Figure 10: A comparison of model performance across different vectorization techniques on the Arabic dataset. Evaluating model performance using a novel metric that measures the time required to generate the vector representations of words varies across the techniques. First, we present that the TF-IDF, Word2 Vec, and FastText experiments are executed on an NVIDIA GeForce MX330 2 GB GDDR5 GPU, while BERT embeddings are generated in the Google Colab environment using an NVIDIA A100 40 GB HBM2 GPU. The performance time metrics, as shown in Table 11, demonstrate that the BERT model consumes more time and resources than the other models across all three datasets. This is due to the transformer architecture, which is more complex than traditional models. Additionally, the TF-IDF method generates vectors that are more than 20 times smaller than those produced by Word2 Vec and FastText because of its dependence on simple statistical calculations that do not require model training or learning processes, unlike the other techniques. Although performance results are presented for English, French, and Arabic datasets, this study does not aim to compare the languages directly. Each language uses distinct pre-trained embeddings with different properties, such as vector dimensionality, corpus origin, and training quality. These variations may affect performance and introduce biases in interpretation. The present study highlights the effectiveness of integrating both classical and deep learning approaches for multilingual sentiment analysis across Arabic, French, and English datasets. Our findings underscore the importance of selecting appropriate feature extraction techniques and classification models when addressing the linguistic diversity inherent in multilingual environments. By employing balanced datasets, this study ensures a fair comparative evaluation and emphasizes the need for language-sensitive sentiment analysis systems, particularly in culturally and linguistically diverse regions such as Morocco. The experimental results reveal that traditional machine learning models, including SVM and NB, perform adequately when combined with conventional vectorization methods such as TF-IDF. However, more semantically rich embedding techniques such as Word2 Vec, FastText, and BERT demonstrate superior classification performance, especially when paired with deep learning architectures like BiLSTM and GRU. Notably, hybrid models (BiLSTM-CNN) and BERT-based pipelines produce better results, confirming the advantage of context-aware representations in capturing nuanced sentiment patterns. The observed variations in model performance across the three languages suggest that sentiment expression is not only syntactically different but also culturally contextual. These variations influence lexical usage, emotional intensity, and polarity, all of which affect model accuracy. In addition, all models were trained and tested on the same data splits, under identical experimental conditions, ensuring fair comparisons. Moreover, the superior results achieved by certain configurations were not marginal but consistently higher than those of other models across all datasets. This reinforces the practical relevance and reliability of the reported improvements. We also acknowledge the importance of applying formal statistical significance testing in scenarios where performance differences are less pronounced or experimental variability is higher.