## 2 **Introduction**
In the first category, numerous text mining and machine learning techniques are employed for the extractions of different UX aspects, such as probabilistic topic models: Latent Dirichlet Allocation (LDA), Probabilistic Latent Semantic Analysis (PLSA) [12, 14, 15], word embedding, aspect-based sentiment analysis [16], and analyzing the relative importance of each extracted UX aspect. In the second category, researchers try to develop a mapping mechanism of all their target extracted UX dimensions on the existing user satisfaction models, such as the Kano model, to give a road map for a product, system, or service improvement or development. We designed a comprehensive framework for modeling UX from online reviews to resolve these challenges. First, we filtered user reviews unrelated to the UX domain using UX multi-criteria qualifiers. Then, we extracted UX aspects from the filtered user reviews using an enhanced topic extraction methodology called UXWE-LDA. UXWE-LDA improves existing knowledge-based topic models by extracting more domain-dependent dimensions in the UX area through UGC. It combines topic modeling, specifically LDA, with word embedding that automatically learns the domain knowledge from a large amount of textual data. The proposed method gains domain knowledge from the vast number of documents using co-occurrence and word-embedding word vectors correlation of related data, resulting in a more coherent topic. Then, sentiment analysis is applied to reviews concerning the extracted UX aspects or dimensions. We aimed to extract the essential aspects by inducing a positive UX that utilized UGC data. Mining UXDs from UGC data allows us to comprehend customer preferences and needs effectively and reliably, allowing the product owner to improve their product design, system, or service. The presented study has potential implications for product design. It can mine the most concerning UX aspects from online reviews, allowing the withdrawal of valuable information for effective product redesign. Furthermore, it can identify the strengths and weaknesses of a product according to the Kano model. This method allows the product designer to understand the different categories of UDXs in the UEQ model, therefore establishing its crucial role in product enhancement. According to the classification results of UXDs, the priority order of UXDs enables developers to plan product enhancements. More specifically, the contributions are made in three parts. * First, the user quality filter module identifies user reviews containing helpful information related to UX. This step is essential to removing trivial user reviews before applying topic modeling. This module classifies online reviews based on predefined UX aspects (user, situation, and product facets). * UXDs extraction from online reviews using proposed user experience word embedding LDA (UXWE-LDA) methodology allows for the automatic learning of the domain knowledge from the given text corpus to generate a more coherent topic. It mainly contains two steps: UXWE-LDA and sentiment analysis. The UXWE-LDA is an improved version of LDA that takes the domain knowledge from the given text corpus, extracts more coherent topics, and assigns labels as UXD to each extracted topic using a dictionary-based approach. Then, it identifies the sentiment orientations of the reviews concerning each UXD based on ensemble methodology. Finally, it classifies each review into positive or negative sentiment categories and associates the sentiment orientation with the extracted UXDs. * The causal relationship of sentiments toward each UXD on user satisfaction obtained from using the Bi-LSTM model to overcome the problem of existing models of review-based user satisfaction studies. The rest of the paper is structured as follows. Section 2 discusses the materials and methods for the analysis conducted. Section 3 describes the results and case study based on the proposed methodology, Section 4 presents a discussion, and Section 5 concludes the work.