## 1 Introduction  
The exponential growth of data generated through websites, social media platforms, online forums, and other digital sources has created substantial opportunities for businesses and decision-makers to refine and innovate their marketing strategies. Effectively leveraging this wealth of information, however, demands advanced processing techniques, particularly those offered by natural language processing (NLP), a subfield of artificial intelligence that focuses on enabling machines to interpret and interact with human language [1]. One key application of NLP is sentiment analysis, also known as opinion mining, which involves examining textual, audio, or visual content to determine the underlying sentiment: positive, negative, or neutral. This technique is crucial in business intelligence, customer relationship management, and strategic marketing, especially in industries such as tourism and hospitality, where understanding customer feedback is essential. The majority of sentiment analysis research is still mostly focused on the English language, ignoring the linguistic diversity present in many regions despite its increasing significance. In Morocco, for example, Arabic is the native language, while French is spoken by roughly 13 million people (around 35% ([[https://www.moroccooworddnews.com/2019/03/268475/international-francophonie-day-moroccan-french](https://www.moroccooworddnews.com/2019/03/268475/international-francophonie-day-moroccan-french)]([https://www.moroccooworddnews.com/2019/03/268475/international-francophonie-day-moroccan-french](https://www.moroccooworddnews.com/2019/03/268475/international-francophonie-day-moroccan-french)), accessed on 24 November 2024) of the population), and English usage has expanded to approximately 30% ([[https://www.britishcouncil.ma/sites/default/files/shift_to_english_in_morocco_16042021_v2.pdf](https://www.britishcouncil.ma/sites/default/files/shift_to_english_in_morocco_16042021_v2.pdf)]([https://www.britishcouncil.ma/sites/default/files/shift_to_english_in_morocco_16042021_v2.pdf](https://www.britishcouncil.ma/sites/default/files/shift_to_english_in_morocco_16042021_v2.pdf)), accessed on 24 November 2024) according to the 2021 British Council report. Given this multilingual landscape, it is essential to consider all three languages when developing effective sentiment analysis tools tailored to the Moroccan context. Sentiment analysis involves several processing stages, among which feature extraction plays a fundamental role. This step converts text into numerical representations that can be interpreted by machine learning models. A wide range of techniques has been developed for this purpose, from traditional methods like bag of words and TF-IDF [2; 3; 4], which rely on statistical frequency-based models, to more advanced approaches such as word embeddings, which capture semantic relationships between words [5; 6]. Recent transformer-based models go even further by generating deep contextual representations of language [7; 8]. In this study, we investigate how various feature extraction methods perform across three languages: Arabic, French, and English, using balanced datasets to ensure fair comparative analysis. We apply four embedding techniques: TF-IDF, Word2 Vec, FastText, and BERT, and assess their effectiveness in combination with multiple machine learning and deep learning classifiers. Specifically, this work aims to:  
1. Use balanced multilingual datasets to ensure comparability across English, French, and Arabic;
2. Apply three categories of feature extraction methods and combine them with both traditional and deep learning classification models;
3. Evaluate model performance across several metrics, including a novel consideration of execution time during embedding generation, allowing us to examine the relationship between vectorization method, classifier, dataset type, and computational efficiency;
4. Provide practical guidance for selecting the most appropriate embedding and classification techniques for multilingual sentiment analysis tasks.