## 5 Conclusions  
The main aim of this paper is to develop an efficient model to discover sentiments associated with different aspects of a given text in order to make a more accurate decision from the users' perspective. The main objectives of the proposed system are: (1) Designing an efficient model to identify and extract all the possible aspects from given textual data. This is achieved by using natural language processing (NLP) to prepare the text in a format adopted by a topic model in addition to a topic model that extracts the main topics/aspects in that text. (2) Mapping between the extracted aspects and their opinions using linguistic and statistical techniques through utilizing a topic model and lexicon classification. (3) Developing a sentiment classification model in order to identify the sentiment orientation of the extracted aspect using an ensemble learning classifier.  
To evaluate the performance of the proposed framework, we have compared each component to the baseline algorithms for the topic modelling, lexicon-based method and ensemble learning classifiers. The results have shown that the proposed framework is able to predict labels of the three review domains--restaurant, movie, and Saudi airlines--with an accuracy of 83.2%, 84% and 84.4% in each domain, respectively. Furthermore, once the proposed system is compared to the baselines algorithms, better results (higher than 2%) were scored in terms of the ability to predict the labels correctly.  
This study has shown some promising results in the field of aspect-based sentiment analysis. It opened the windows wide for further research to enhance and expand this area of research. For future research, the proposed framework could be expanded to handle Arabic texts, which will be a challenging task. Likewise, future studies could apply more resources to the proposed framework to further enhance the results.  
Conceptualization, S.K. and M.A.; methodology, S.K. and N.A.; software, N.A.; validation, N.A.; formal analysis, N.A.; investigation, N.A. and S.K.; resources, N.A., M.A. and S.K.; data curation, N.A.; writing--original draft preparation, N.A.; writing--review and editing, S.K.; visualization, N.A.; supervision, S.K. and M.A.; funding acquisition, M.A.; project administration. M.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through project number 523.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Domain** & **Ensemble Method** & **Acc. (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{Restaurant} & Bagging & 80.3 & 80.1 & 79 & 80.1 \\  & AdaBoost & 79.5 & 81 & 76.4 & 78.8 \\  & Majority Voting & 77.5 & 76.4 & 79.4 & 77.9 \\  & Stacked Generalization (Proposed) & **83.2** & **83** & **82.4** & **83.1** \\ \hline \multirow{4}{*}{Movie} & Bagging & 80 & 80.7 & 79.6 & 79.4 \\  & AdaBoost & 77.8 & 79 & 77.7 & 77.7 \\  & Majority Voting & 77.6 & 78 & 77.6 & 77.2 \\  & Stacked Generalization (Proposed) & **84** & **83.5** & **83** & **84** \\ \hline \multirow{4}{*}{The Domestic Saudi Airlines} & Bagging & 74.1 & 74 & 74.3 & 74 \\  & AdaBoost & 77.5 & 77.3 & 76.2 & 76.3 \\ \cline{1-1}  & Majority Voting & 75 & 74 & 75.3 & 75.7 \\ \cline{1-1}  & Stacked Generalization (Proposed) & **84.4** & **83.1** & **82** & **84.7** \\ \hline \end{tabular}
\end{table}
Table 7: Performance comparison of different ensemble methods with proposed method on restaurant reviews.