## 2 Related Work
### Topic Modeling  
An unsupervised learning approach for data analysis was used to evaluate text and various unlabeled data ([PERSON] et al., 2018). Topic modeling retrieves information to infer the latent themes in a variety of documents automatically, allowing for the organization, comprehension, and summarization of massive amounts of textual material. In addition, a lot of researchers employ topic models to evaluate data since these models offer an understandable representation of documents used in various natural language processing (NLP) activities ([PERSON] et al., 2018). [PERSON] et al. (2018) determined the research areas that have been investigated in the field of biochemistry over the past 20 years and analyzed how these areas have evolved using the latent Dirichlet allocation (LDA) technique for topic modeling. Meanwhile, [PERSON] et al. (2018) combined non-negative matrix factorization (NMF), a type of topic modeling approach, with deep neural networks in the recommendation system to study the relationship among users and the products themselves. In the same recommendation system domain, [PERSON] et al. (2018) integrated topic modeling with browser history and Wikipedia data to tailor users' content through topic extraction, while [PERSON] et al. (2018) employed keyATM, a topic modeling method, to extract significant information from app reviews. [PERSON] et al. [37] conducted an experimental research study to evaluate an emerging modeling type called BERTopic alongside NMF and LDA using the Arabic language. Their findings showed that BERTopic outperformed LDA and NMF. To improve user experiences, [PERSON] et al. [38] mined 104,109 reviews of ten Indian sites to learn about visitors' experiences and evaluate data to enhance service quality. The data were obtained from the Indian version of the TripAdvisor website utilizing the Scrapy framework. They then preprocessed and tokenized the data before performing lexicon-based sentiment analysis with the AFINN Lexicon dictionary. They used NMF to derive the topics. Their results showed that five stars was the most popular rating, with the Taj Mahal being the most visited place. The topic modeling grouped topics based on the ten locations, resulting in various themes for each site. The extracted topics were mostly about activities, views, and provided services. The researchers in [39; 40] studied the hospitality industry and employed topic modeling approaches to assess user experience. [PERSON] et al. [39] investigated the primary themes of interest for Korean accommodation users in South Korea by analyzing 104,161 English reviews of online travel agencies obtained from Agoda, Booking,com, and hotel websites in 2018. These opinions were organized by accommodation location and type. Furthermore, they employed the LDA topic model to generate 14 topics and validate them in four phases. Meanwhile, [PERSON] et al. [40] aimed to identify the causes of consumer complaints in an attempt to assist managers of hotels in New York City to enhance customer satisfaction, service quality, and revenue. They collected 27,864 English reviews from 315 different-grade hotels on the TripAdvisor website. The text was then preprocessed to employ the structural topic model (STM) to compare the topics included in positive and negative reviews. As a result, they identified 10 negative topics expressing customer dissatisfaction, which can be described as issues related to the location in low-end hotels, issues related to services and prices in high-end hotels, and issues related to booking, room type, and cancellation in all hotels. Also, [PERSON] et al. [41] used LDA to compare Airbnb feedback from Singapore and Hong Kong. They believed in the ability of topic modeling to uncover underlying themes from customer opinions. To acquire the dataset, they gathered Airbnb customer reviews in English from Singapore and Hong Kong. Following that, they removed any extraneous data from the dataset before stemming to the root.