## 2 Related Work
### Clustering  
The unsupervised machine learning approach identifies and groups similar data points in huge datasets without regard for specified outcomes. It divides objects into clusters that are more similar than other groupings. It is commonly used as a data analysis tool to identify patterns or interesting trends in data, such as groupings of consumers based on their behavior. Clustering may be used in a wide range of applications, including e-commerce, cybersecurity, mobile data processing, user modeling, health analytics, and behavioral analytics [23]. Several researchers have used clustering algorithms to mine user reviews. [PERSON] et al. [43] used public feedback on 46 commercially available applications found on the Apple and Google Play Stores to identify user experience concerns that will influence future app development for stroke caregiving. These researchers preprocessed and filtered the collected data to obtain 13,368 English reviews. Moreover, they applied the TF-IDF method and k-means clustering to extract 26 clusters classified into seven user experience dimensions: usefulness, usability, desirability, accessibility, findability, value of the app, and credibility. This study highlighted various user experience concerns as a result of the app developers' failure to grasp the user's demands. Furthermore, this study demonstrates the use of a participatory design method to encourage better knowledge of user demands, hence reducing difficulties and ensuring long-term usage. Also, [PERSON] et al. [44] used 564 English user reviews from two hotels on the TripAdvisor website for text summarization. These researchers cleaned the data and used part of speech (POS) tagging to obtain the total sentence similarity, which was calculated by multiplying sentiment similarity based on nouns and content similarity based on adjectives. Then, they applied k-means and Gaussian mixture mode (GMM) clustering. As a result, GMM outperforms k-means when k = 5 in terms of summary information and sentence variety. Moreover, [PERSON] et al. [45] mined 20,461 English user reviews after cleaning for unnecessary data. The researchers gathered the dataset from seven wellbeing and mental health chatbots to discover user experience issues. Then, they used TF-IDF vectorization to obtain the word weight for each review, which was utilized as an input for the k-means clusters to identify comparable reviews based on their content. They attained the optimal number of clusters using the elbow method in k = 6. Most of the cluster's top ten words represented positive sentiments, while negative words were rare. These findings can be utilized to offer recommendations for mental health app developers. By combining the previous techniques for sentiment analysis, topic modeling, and clustering, [PERSON] et al. [46] evaluated mobile banking application feedback using LDA topic modeling and NB sentiment analysis. These researchers gathered 6194 Indonesian comments from the mobile banking app available in the Google Play Store in 2019 and manually annotated them, grouping them into 3021 negative and 3173 positive comments. They built and verified the NB model using the k-fold cross-validation technique with k-values of 3, 4, and 5. Meanwhile, the LDA model was built using the bigram, trigram, and corpus dictionary. The LDA model was then trained and assessed based on its coherence score. Consequently, the NB model performed best in the third test with k = 5, achieving 86.76% accuracy, 93.47% recall, and 92.48% precision. Contrastingly, the best coherence score was attained on 20 clusters: 0.480691 for the positive class and 0.366344 for the negative class. The authors of this study were able to identify the most essential parts of the text in both positive and negative comments. As a result, the application's advantages were its simplicity, ease of use, and helpfulness, while the application's shortcomings included its inability to deliver OTP codes, issues with the registration and login processes, and lost connections. Also, [PERSON] et al.