# IWF-TextRank Keyword Extraction Algorithm Modelling
## 3 Results
### Comparison of Results
#### 3.3.3 Comparison of Results of Different Extraction Methods  
In order to further verify the extraction effect of the IWF-TextRank algorithm, it was compared with that of the traditional TextRank algorithm as well as the TFIDF algorithm, and the extraction results are shown in Table 13 and Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Arithmetic**} & \multicolumn{4}{c}{**Accuracy (P)**} & \multicolumn{4}{c}{**Recall (R)**} & \multicolumn{4}{c}{**F-Value**} \\ \cline{2-13}  & **3** & **5** & **7** & **10** & **3** & **5** & **7** & **10** & **3** & **5** & **7** & **10** \\ \hline TFIDF & 0.685 & 0.632 & 0.601 & 0.543 & 0.189 & 0.284 & 0.399 & 0.536 & 0.296 & 0.392 & 0.480 & 0.539 \\ TextRank & 0.692 & 0.643 & 0.608 & 0.557 & 0.201 & 0.297 & 0.406 & 0.545 & 0.311 & 0.406 & 0.487 & 0.551 \\ IWF-TextRank & 0.732 & 0.704 & 0.652 & 0.613 & 0.229 & 0.357 & 0.456 & 0.584 & 0.348 & 0.473 & 0.537 & 0.598 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Table of results for different algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & \(\alpha\) & \(\beta\) & \(\gamma\) & \(\delta\) \\ \hline
1 & 0.25 & 0.25 & 0.25 & 0.25 \\
2 & 0.31 & 0.34 & 0.22 & 0.13 \\
3 & 0.34 & 0.35 & 0.20 & 0.11 \\
4 & 0.35 & 0.35 & 0.19 & 0.11 \\
5 & 0.33 & 0.35 & 0.21 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Parameter settings.  
As can be seen from the figure, when the number of extracted keywords is 3, 5, 7, 10, the extraction effect of the TFIDF algorithm based on word frequency consideration is relatively poor compared to the other two methods; relative to the TFIDF algorithm, the keyword extraction effect of the traditional TextRank algorithm has been improved to a certain extent, and the extraction effect of the IWF-TextRank algorithm is significantly better than the other two methods. Specifically, there is a 10.06% improvement in accuracy over TextRank and an 7.16% improvement in recall when the number of keywords is 10. These enhancements were validated by a statistical significance test (\(p<0.05\)), indicating that the observed improvements are statistically robust and not due to random variation.  
The integration of semantic features with the BP neural network plays a pivotal role in enhancing the algorithm's performance. By capturing deeper contextual relationships between terms, the BP neural network optimises the weight distribution of features, leading to the more accurate identification of domain-specific keywords. Furthermore, the improvement in the F-value demonstrates that the IWF-TextRank algorithm effectively balances the inherent trade-off between precision and recall, resulting in a more comprehensive and reliable keyword extraction process. This balance is particularly advantageous for traffic accident data analysis, where the precise extraction of relevant keywords is critical for accurate summarisation and further analytical tasks.  
Figure 5: Comparison of different parameters. (**a**) Comparison of \(p\)-values for different parameters; (**b**) comparison of R-values for different parameters; (**c**) comparison of F-values for different parameters.