# IWF-TextRank Keyword Extraction Algorithm Modelling
## 3 Results
### Comparison of Results  
#### 3.3.1 Comparison of Results for Different Features  
(1) Comparison of results for single features  
The keyword extraction experiment under single-feature conditions can not only be used to compare and analyse the results of multi-factor and single-factor extraction, but can also preliminarily verify which feature enhancement can more effectively improve the accuracy of keyword extraction. The feature parameter settings are shown in Table 7, in which groups 1-4 represent the parameter settings when the single feature is word frequency, word nature, word length, and semantics, respectively, and group 9 is the parameter settings under the selected feature conditions in this paper. The extraction results are shown in Table 8 and Figure 3, where (a), (b), and (c) represent the results for the \(p\)-value, R-value, and F-value under different conditions, respectively. As can be seen from the figure, when the number of extracted keywords is 3, 5, 7, or 10, respectively, the extraction effect under the condition of multi-feature combination proposed in this paper is significantly better than that under the condition of a single feature; the comparative analysis of the extraction effect between individual features shows that, among them, lexicality is the optimal result for the feature, followed by word frequency, and then word length. Its \(p\)-value, R-value, and F-value are significantly higher than those when semantics are used as a single feature, indicating that word frequency and lexical features have a greater impact on the keyword extraction effect. The word length feature has a relatively smaller impact on the keyword extraction effect, while the extraction effect is the worst when the algorithm is improved only with semantics as a feature; thus, semantic features have a smaller impact on the extraction result, which also confirms the results of parameter training from the side. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Arithmetic**} & \multicolumn{3}{c}{**Accuracy (P)**} & \multicolumn{3}{c}{**Recall (R)**} & \multicolumn{3}{c}{**F-Value**} \\ \cline{2-10}  & **3** & **5** & **7** & **10** & **3** & **5** & **7** & **10** & **3** & **5** & **7** & **10** \\ \hline
1 & 0.713 & 0.683 & 0.631 & 0.592 & 0.207 & 0.338 & 0.438 & 0.562 & 0.321 & 0.452 & 0.518 & 0.576 \\
2 & 0.719 & 0.687 & 0.637 & 0.599 & 0.211 & 0.340 & 0.440 & 0.566 & 0.326 & 0.454 & 0.520 & 0.582 \\
3 & 0.702 & 0.667 & 0.623 & 0.585 & 0.205 & 0.334 & 0.434 & 0.557 & 0.317 & 0.445 & 0.511 & 0.580 \\
4 & 0.694 & 0.649 & 0.612 & 0.564 & 0.202 & 0.325 & 0.428 & 0.551 & 0.313 & 0.433 & 0.504 & 0.557 \\
9 & 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 8: Table of single-feature results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
\begin{tabular}{c} **Serial** \\ **Number** \\ \end{tabular} & **Diagnostic Property** & \(\alpha\) & \(\beta\) & \(\gamma\) & \(\delta\) \\ \hline
1 & Word frequency & 1.00 & 0.00 & 0.00 & 0.00 \\
2 & Part of speech (noun, verb, adjective etc.) & 0.00 & 1.00 & 0.00 & 0.00 \\
3 & Word length & 0.00 & 0.00 & 1.00 & 0.00 \\
4 & Meaning of words & 0.00 & 0.00 & 0.00 & 1.00 \\
9 & Word frequency—lexicality—word length—semantics & 0.33 & 0.35 & 0.21 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Single-feature parameter settings. (2) Comparison of results for multiple features  
According to the results of the single-feature experiments, it can be seen that lexicality has a greater impact on the keyword extraction results, followed by word frequency, and then word length, while semantic features have a smaller impact. Therefore, the lexicality, word frequency, and word length features that have a greater impact were selected to carry out the double-feature combination experiments and triple-feature combination experiments, respectively, and the results were compared and analysed with the results of the textual feature conditions.