###### Abstract  
User-generated content on numerous sites is indicative of users' sentiment towards many issues, from daily food intake to using new products. amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users' feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers' opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications.  
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Footnote †: journal: Medical Image Computing  
1  
e1 Department of Information Systems, King Faisal University, Al-Ahsa 31982, Saudi Arabia; [EMAIL_ADDRESS]  
2 School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Suzhou 215000, China; [PERSON]
However, predicting overall polarities for each review is not enough since the review could provide comments on various aspects of the corresponding product or service. For instance, one review about a restaurant may mention the prices, cleanliness, services and more. Analysing these aspects, rather than the overall review, constructs a better understanding of the exact leading pros and cons of the product or service. Therefore, the study focuses on performing aspect-level sentiment classification that predicts every aspect.  
This paper proposes a multi-aspect-oriented sentiment classification model by using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). In this study, the multi-aspect sentiment analysis is addressed by using a topic model and an ensemble learning method. However, the challenge of the models is that documents are rich in excessively informal and colloquial language. Thus, this research aims to identify an approach that depends on the combination of probabilistic topic modelling, namely, Seeded Aspect Latent Dirichlet Allocation (SA-LDA) and an ensemble learning method, to analyse and visualize noticeable aspects of text documents and classify them afterwards. Different domains, methods and classifiers have been used to address the aspect extraction and sentiment analysis tasks.  
Furthermore, to evaluate the effectiveness of the proposed model, we conduct extensive experiments on three different domains of online reviews (movie, restaurant, and domestic Saudi Airlines reviews). The proposed model shows promising results. As far as we know, no previous research has proposed a model similar to our proposed one, which consists of three main modules: (1) LDA-based topic modelling; (2) sentiment lexicon (SentiWordNet); (3) the ensemble classifier (stacked generalization method).  
Section 2 of this paper describes several related works, whereas the description of data collection, multi-aspect extraction model, proposed methodology, and ensemble learning algorithm are presented in Section 3. The findings and the conclusion with future works are presented in Sections 4 and 5, respectively.