## 4 Results
### Topic Modeling and Clustering
Optimization  
For the sake of improving the clustering outcomes, this research work utilized the POS tagging method offered by the CAMeL tool, which achieved 95.5% accuracy in predicting the correct POS. Furthermore, POS tagging is a widely used technique in NLP that involves classifying words in a text (corpus) according to a certain part of speech, based on the word's meaning and context. Additionally, it is used to characterize the lexical words in a text \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topic Number** & **Topic Name** & **Evaluation Metric** & **Silhouette Score of Reviews Using Doc2 Vec** & **Cluster Result Using PCA Visualization** \\ \hline \hline
0 & \
\begin{tabular}{c} Delivery and \\ Payment \\ \end{tabular} &
\begin{tabular}{c} Euclidean \\ distance \\ \end{tabular} & 0.68 & \\
0 & \
\begin{tabular}{c} Delivery and \\ Payment \\ \end{tabular} &
\begin{tabular}{c} Cosine \\ similarity \\ \end{tabular} & 0.63 & \\ \hline
1 & \
\begin{tabular}{c} Customer \\ support and \\ updates \\ \end{tabular} &
\begin{tabular}{c} Euclidean \\ distance \\ \end{tabular} & 0.72 & \\
2 & \
\begin{tabular}{c} Prices \\ \end{tabular} &
\begin{tabular}{c} Cosine \\ similarity \\ \end{tabular} & 0.67 & \\ \hline
3 & \
\begin{tabular}{c} Euclidean \\ distance \\ \end{tabular} & 0.76 & \\
\begin{tabular}{c} Cosine \\ similarity \\ \end{tabular} & 0.63 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental result of the Mrsool dataset using k-means within Doc2 Vec. Figure 11: Clustering topics using (**a**) Euclidean distance and (**b**) cosine similarity. by categorizing them as verbs, nouns, adjectives, and adverbs [63]. This method allows models to better comprehend and learn human language by improving their understanding of word structure and meaning. Therefore, the key rationale for employing this step is to restrict the clustering process to words that convey sentiments. Hence, only the nouns and adjectives were employed in this experiment. After applying POS tagging with the aim of improving the model's performance, Table 8 demonstrates that the optimization process had an influence on the cosine similarity metric in the first two topics: delivery and payment and customer support and updates. However, the performance of these topics remained steady when utilizing the Euclidean distance metric. A noticeable improvement was found in the topic of prices, in which it appeared that the Euclidean metric increased by 1%, while the cosine similarity increased by 20%, where the first metric continued to outperform the second by 70% to 58%. Furthermore, it appeared that the clustering process for the application topic decreased slightly in both metrics, with the Euclidean distance continuing to outperform cosine similarity by 74% to 46%. Considering the results summarized in Table 7, it remained for us to explore whether the POS technique led to forcing the clusters to classify reviews into positive and negative groups. From Figure 12, despite the optimized results, it appears that the clustering process was dependent on other factors within the topic, which included both positive and negative sentiments. Hence, we conclude that using the first approach combining topic modeling and sentiment analysis achieved the research objectives, and this is what we observed during the testing phase.