# Analysis of Short Texts Using Intelligent Clustering Methods
## 3 Results and Discussion
The BERT model shows an accuracy of about 60%. Although it can cope with contextual relationships between words, its limitations in classifying texts that require precise topic extraction reduce the overall effectiveness. The TF-IDF model shows higher accuracy, about 75%. It is based on the frequency of words in the document and their significance, which improves its accuracy compared to BERT. However, the lack of consideration of contextual relationships between words is the main limitation of the method, especially when working with texts that require a deep understanding of the semantics. The hybrid LDA + BERT + AE model shows a significantly better result, with an accuracy of about 98%. This is explained by the fact that this model uses the strengths of LDA for topic modeling, BERT for contextual analysis, and Autoencoder for data dimensionality reduction. The result is a model with high classification accuracy, especially effective when analyzing texts with a clearly expressed topic focus. Figure 10 displays the comparison of the F1 scores of the same models. The measure considers precision and recall, providing a balanced view of the model's performance. The BERT model possesses an F1-score of around 0.55, which shows its ability to interpret context, but it falls short in accurately classifying texts, especially when dealing with specific categories. The TF-IDF model shows a better result, with an F1-score of about 0.7, and has a more suitable precision-recall ratio than BERT. However, the lack of understanding of deep semantic relationships limits the classification of complex texts. Compared with the other four models, the hybrid LDA + BERT + AE model achieves an F1-score of  
Figure 8: Change in the loss value during the training of the hybrid LDA + BERT + AE model. Figure 9: Results for accuracy of models. around 0.9, confirming that it performs very well in accurately classifying texts. Extracting topic aspects and contextual nuances achieves higher precision and recall, so it is a good choice for short text analysis tasks. In conclusion, in both cases, the LDA + BERT + AE model significantly outperforms the BERT and TF-IDF models regarding both accuracy and F1-score. Although BERT provides contextual understanding and TF-IDF effectively handles keyword importance, its limitations become apparent when classifying texts that require deep semantic analysis. The hybrid LDA + BERT + AE model, combining the best features of different methods, provides maximum accuracy and efficiency in context-rich text analysis tasks. Figure 11 shows the text analysis results regarding [PERSON] using various text processing methods: BERT, TF-IDF, and LDA + BERT + AE. The BERT method identifies \"entertainment\" as the dominant theme (32.11%), corresponding to the text's central theme of music and entertainment. TF-IDF and LDA + BERT + AE distribute weights more evenly between categories, although LDA + BERT + AE is more accurate in identifying \"entertainment\" (51.51%) as the top category. This shows how integrating contextual understanding with topic modeling can improve text classification. We used measures such as the Jaccard Index, Matthus Correlation Coefficient (MCC), Foulkes-Mallows Index (FM), and Cohen's Kappa Coefficient to quantify the performance of models. These measures helped us compare and evaluate the classification performance of different methods in depth, providing a comprehensive understanding of their performance and accuracy. The metrics results show that the LDA + BERT + AE method performs better on most metrics, indicating its superiority in classifying texts (Table 2). \begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **LDA + BERT + AE** & **TF-IDF** \\ \hline Jaccard Index & 0.037579 & 0.010073 \\ Matthews Correlation Coef. & \(-\)0.164604 & \(-\)0.253621 \\ Foulkes–Mallows Index & 0.513597 & 0.537451 \\ [PERSON]’s Kappa Coefficient & \(-\)0.164259 & \(-\)0.233235 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results metrics. Figure 10: F1-score results of models.