## 4 Results
### Topic Modeling and Clustering  
Based on the assumption that reviews within each topic can be grouped into positive and negative clusters depending on the vectors acquired from the document embedding, the second approach applied the k-means clustering algorithm to help classify these reviews into positive and negative groups. The main idea behind using the k-means clustering algorithm is the ability to handle unstructured data that construct between 80% and 90% of the world-generated data without the additional costs that structured data entail [62]. In addition, the k-means clustering algorithm is a valuable tool for obtaining insights from textual data, thanks to its simplicity, effectiveness, and efficiency with regard to computation time. Hence, in the first step in building such a model, the neutral comments obtained from the CAMeL tool were eliminated, and only positive and negative comments were used to avoid confusing the clusters with neutral reviews, based on the assumption that the reviews will be automatically divided into positive and negative categories without manual involvement. After k-means clustering, the sentiment polarity score obtained from  
Figure 9: Topic-based sentiment analysis of Mrsool dataset. the first approach using the CAMeL tool was used and compared with the clustering result via sunburst chart visualization to determine whether the assumption was true. Before choosing the optimal number of k-clusters, considering the hyperparameter of the k-means algorithm, it is important to mention that Doc2 Vec embedding vectors were used as the input into the k-means cluster. Hopefully, the semantic meaning of the sentiment words was captured and then classified by k-means appropriately. In the k-means algorithm, we must first predefine the number of k that could be discovered using the elbow method. Thus, from Figure 10, we can notice an elbow form on the graph, which helps in selecting the k-value at which the elbow is produced. This can be referred to as the elbow point. Beyond this point, raising the 'k' number does not result in a substantial reduction in the WCSS. As a result, it can obviously be determined that the optimal number of clusters is k = 2 for all extracted topics, which provides a good indication to confirm our the assumption. Table 7 presents the experiments that applied the k-means cluster fed with Doc2 Vec vectors of the reviews, showing the silhouette score computations using the _Euclidean distance_ and _cosine similarity_ metrics, in addition to the reduced cluster results visualized using PCA and scatter plots for every extracted topic in the Mrsool dataset. As a result, the clustering process appears to have been successful using both matrices due to the good results achieved and the way that the data points are well separated from each other in the graph, which was confirmed by the elbow method. We note that the clustering process using the Euclidean distance metric Figure 11a showed better results than the cosine similarity metric Figure 11b. The next step would be to use the results of the sentiment polarity score obtained from the CAMeL tool, which will assist in interpreting the cluster's content and discovering whether it fulfilled its desired purpose of dividing reviews into positive and negative clusters. By looking at the sunburst chart that is shown in Figure 11, containing three layers of data, namely, the topic number, clusters, and sentiment scores, it can be seen that all clusters involved both positive and negative comments in varying proportions for every topic. This observation could be due to the fact that the clustering method may have depended on the context or syntax of the comments rather than focusing on the positive and negative words due to the varying length of the comments and them containing various POSs. Figure 10: The elbow method of (**a**) topic 0 (**b**) topic 1 (**c**) topic 2 and (**d**) topic 3.