## 2 Related Work
### Clustering
[47] used document clustering, topic modeling, and opinion-mining techniques to assess and characterize public opinion based on Twitter reviews of Uber customer service, using a dataset of 215,387 English-language tweetsfrom the year 2020. Several topic models were used, including the NMF, latent semantic analysis (LSI), and LDA models, where the optimal topic number was determined using the Gibbs sampling approach. They then used different document clustering approaches, including k-means clustering and the genetic with local convergence method, to merge tweets into one of the specified topics. LDA and NMF beat the LSI, obtaining seven topics: Uber account/Uber app, Uber drivers, money and payments, Uber Eats, opinions about customer service, time and cancellation, and interaction with Uber support. Using the elbow technique, they discovered that k = 7 represented the optimal clustering value, which was consistent with the number of LDA topics. Finally, they studied and discussed the attitudes and emotions associated with each extracted topic. In general, machine learning techniques like sentiment analysis, topic modeling, and clustering have been proven to be effective tools in analyzing user experience. By utilizing these methods, businesses can gain valuable insights into how users perceive their services and make informed decisions to improve the user experience. Previous studies have predominantly employed LDA for topic modeling, SVM and NB for sentiment analysis, and k-means for clustering as seen in Table 1. However, there is a noticeable lack of research that combines these methodologies to uncover the crucial factors influencing user experience and explore user sentiments in relation to these factors. Notably, most of these studies have mainly focused on the English language. Therefore, this study aims to address this research gap by conducting an Arabic text mining study, specifically targeting the super application environment in the Kingdom of Saudi Arabia. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref** & **Year** & **Methodology** & **Environment** & **Super** & **Country** & **Language** & **Results** & **Limitations** \\ \hline
[24] & 2022 & Sentiment analysis & Gov2 Go app & Yes & Indonesia & English & 97\% for LSTM, 94.22\% for NB & Data only collected from the Google Play Store \\ \hline
[25] & 2019 & Sentiment analysis & Ovo app & Yes & Indonesia & Indonesian & 82.89\% for SVM & Small dataset \\ \hline
[26] & 2022 & Sentiment analysis & Eight banking services’ applications & No & Yemen & Arabic & 89.65\% for NB & Data only collected from the Google Play Store, using an unbalanced dataset \\ \hline
[27] & 2022 & Sentiment analysis & Tawakkalna, Tabaud, Sehhaty, Sawid, and Sehhah apps & Yes & Saudi Arabia & Arabic & 78.46\% & Using unbalanced dataset \\ \hline
[28] & 2022 & Sentiment analysis & Tawakkalna, Tabaud, Sahhaty, Sawid, and Sehhah apps & Yes & Saudi Arabia & Arabic converted to English & 94.38\% for SVM & Using Google translate Arabic reviews \\ \hline
[29] & 2023 & Sentiment analysis & Saudi & Ministry of Commerce’s X account and Commercial report app & No & Saudi Arabia & Arabic & 94\% for SVM & Unbalanced dataset \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of machine learning approaches to analyze user experience. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref** & **Year** & **Methodology** & **Environment** & \begin{tabular}{c} **Super** \\ **App** \\ \end{tabular}  & **Country** & **Language** & **Results** & **Limitations** \\ \hline
[30] & 2022 & \begin{tabular}{c} Aspect-based \\ sentiment \\ analysis \\ \end{tabular}  & \ \begin{tabular}{c} Jearan \\ restaurant’s \\ website \\ \end{tabular}  & No & Jordan & Arabic & 84.47\% for SVM & \begin{tabular}{c} Unbalanced \\ dataset \\ \end{tabular}  \\ \hline
[38] & 2021 & \begin{tabular}{c} Lexicon-based \\ sentiment analysis and topic \\ modeling \\ \end{tabular}  & TripAdvisor & No & India & English & \