## 3 Methodology
### Data Processing  
The collected data must go through important preprocessing steps, including several techniques for cleaning and normalizing. The process begins with tokenization, followed  
Figure 1: The proposed methodology.  
by the removal of special characters such as URLs, HTML tags, stop words, and punctuation. Additionally, stemming and lemmatization techniques are applied to further normalize the text. For both the English and French datasets, as shown in Table 2, we follow the same preprocessing steps: first, we convert all text to lowercase, then perform word-by-word tokenization. Next, we remove stop words, punctuation, URLs, HTML tags, and numbers. Finally, we apply stemming using the Porter Stemmer as the word normalizer.  
The Arabic dataset as presented in Table 3 requires special processing due to the unique structure of the language. First, we remove the \"Tashkil,\" which represents vowel marks in Arabic. For example, the phrase '(meaning \"Indeed, this is a text with vowel marks\") becomes '(without vowel marks). We also normalize certain letters by converting variations like '(r)' into '(r). Additionally, we remove the \"Tatwil\" (elongation marks), so '(r)' becomes '(r)'(meaning \"thank you\").  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Text** & **Language** & **Text Cleaning** \\ \hline Room was very well appointed and facilities were good & English & room well appoint facil good \\ Large queues for breakfast Room was very & & \\ crammed booked a room for 2 adults and 2 & & \\ children no space to go in with a pushchair Not & English & \\ enough lifts to cater for guests at times it took us & & \\
10 min to go down to the lobby restaurant\(\backslash\)r & & \\ Were staying in london for just one night and the & & \\ location was perfect we paid around 200 for one & & \\ night for a family suite and it was very well worth & & \\ the money\(\backslash\)r & & \\ Chambre de petite taille Le petit-dejeuner à & & \\ ameliorer Manque d’equipement & & \\ Le service, le personnel et la proprete des lieux. & French & \\ Hôtel de qualite à proximité immédiate de la gare. & French & Hôtel qualite proximité immédiat gare \\ \hline \hline \end{tabular}
\end{table}
Table 2: Text cleaning in English and French datasets.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Text** & **Text in English** & **Text Cleaning** & **Tashaphyne** & **ISRI Stemmer** \\ \hline \hline \multicolumn{3}{l}{I didn’t like anything. What we & \\ \(\begin{array}{l}\text{U}\text{I}\text{I}\text{I  
We eliminate repeated letters, such as changing '\(\mathbf{\varsigma}^{\text{\scalebox{1.07}{$\mathbf{\varsigma}$}}||||}\mathbf{\cdot}\)? to '\(\mathbf{\varsigma}^{\text{\scalebox{1.07}{$\mathbf{\varsigma}$}}}\mathbf{\cdot}\mathbf{\cdot}\mathbf{\cdot}\)? (meaning 'worderful'). Moreover, we remove Arabic stop words while keeping negation terms like {\"?\", \"\(\mathbf{\varsigma}\)\", \"\(\mathbf{\varsigma}\)\", \"\(\mathbf{\varsigma}\)\", \"\(\mathbf{\varsigma}\)\", \"\(\mathbf{\varsigma}\)\", \"\(\mathbf{\varsigma}\)\", \"\(\mathbf{\varsigma}\)\", \"\(\mathbf{\varsigma}\)\"]. Finally, we remove punctuation from the text. For normalization, we apply stemming techniques. In Arabic, there are several options, such as Khoja, Light10, Tashafyne, ISRI, FARASA, QARAB, and MADAMIRA. In our work, we use two techniques: Tashafyne and ISRIStemmer. This choice was motivated by the need to balance computational efficiency and effective preprocessing across all three languages studied. Although these methods do not capture deep morphological structures such as lemmatization or disambiguation, they have proven sufficient to reduce surface-level variability in Arabic text and ensure consistency with the multilingual approach.