# Two-Stage Dimensionality Reduction for Social Media Engagement Classification
## 5 Conclusions  
In this study, a two-stage dimensionality reduction (TSDR) method was proposed for data classification. The method involves two stages: extracting high-quality features by maximizing the pairwise separation probability and transforming the resulting subset into a \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measure** & **Original** & **Stage 1** & **Stage 2** \\ \hline Dimensions & 11 & 8 & 2 \\ Silhouette coefficient & 0.12 & 0.19 & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters for Instagram dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multicolumn{4}{c}{**Original Dataset**} & \multicolumn{4}{c}{**TSDR Subset**} \\ \cline{2-7}  & \(k\)**-nn** & **MLP** & **LDA** & \(k\)**-nn** & **MLP** & **LDA** & **TSDR \(f\)** \\ \hline Accuracy & 73.17\% & 74.78\% & 76.77\% & 75.91\% & 75.00\% & **77.11\%** & 76.84\% \\ F1 score & 66.49\% & 70.34\% & 72.28\% & 71.34\% & 70.22\% & 72.55\% & **73.66**\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification performance using Instagram dataset. reduced final space by maximizing the distance between the cluster centers and minimizing dispersion within the same class. The proposed method was tested on benchmark datasets and showed improved accuracy and F1 scores compared to the original datasets when used as a preprocessor or a classifier. The results indicate that the higher the number of attributes is, the more the proposed method benefits from dimensionality reduction. The study also shows that the use of the TSDR approach leads to more distinguishable and separable clusters, as indicated by the significant improvement in the silhouette coefficient values. The use of the TSDR approach requires additional computational time, but the larger the dataset is, the smaller the overall time for classification becomes when compared to a simple neural network. As the culmination of our research, it is essential to highlight the evolution of our work and the trajectory it has taken. While the current article delves into the successful application of TSDR and its comparison with various classifiers, we recognize that dimensionality reduction itself warrants an in-depth exploration of different methods and their comparative efficacy. As we plan to improve our existing proposal, our intention is to offer the research community a detailed examination of the nuances and strengths of various dimensionality reduction techniques, providing valuable insights for future endeavors in social data analysis. This research also complements the results of a previous study on a social media dataset [32], which showed that the application of the TSDR algorithm improved the accuracy from 73% to 77%, even with a reduced dimension set. The method may also reduce the overall time required for classification. This result also highlights the effectiveness of the proposed algorithm in improving the performance of machine learning tasks. Moreover, it confirms the method's potential regarding its application to real-life data. In assessing the performance metrics of our classifier, it is crucial to contextualize the level of accuracy achieved. Social media datasets, particularly those derived from platforms like Instagram, are inherently dynamic, characterized by randomness and sparsity. The ability to attain a 77% accuracy in classifying such complex and diverse social data is a testament to the robustness of our two-state dimensionality reduction (TSDR) algorithm. It is essential to recognize the inherent challenges posed by the nature of social media content, where patterns and trends can emerge unpredictably. Moreover, the noteworthy improvement from our previous work, where accuracy stood at 73%, underscores the efficacy of the enhancements introduced in this manuscript. This incremental progress represents a substantial step forward, and the level of accuracy achieved holds considerable significance within the intricate landscape of social data analysis.