# IWF-TextRank Keyword Extraction Algorithm Modelling
## 1 Introduction
This improved approach aims to provide more accurate domain-specific keyword extraction, addressing the shortcomings of traditional algorithms such as TF-IDF and basic TextRank, which cannot take into account semantic relationships and contextual importance. Experimental results show that the IWF-TextRank algorithm outperforms traditional methods such as TF-IDF and basic TextRank in terms of keyword extraction accuracy, providing an effective solution for keyword extraction tasks in fields such as traffic analysis. Keyword extraction methods are mainly classified into three categories: statistical-feature-based methods, topic-based modelling methods, and word graph modelling-based methods. Among the statistical-based methods, TF-IDF [8] is a widely used technique where keywords are ranked based on word frequency and inverse document frequency. However, TF-IDF has been criticised for its over-reliance on word frequency, especially in professional domains, which often leads to poor extraction results [9]. To address this issue, researchers have proposed various improvements, such as integrating positional and word span weights [10], or combining TF-IDF with weight-balancing algorithms [11]. Topic-based modelling approaches, such as Latent Dirichlet Allocation (LDA [12]), have also been used for keyword extraction. LDA performs well in capturing semantic associations between words, making it a powerful tool for extracting topic-relevant keywords from large text corpora [13; 14]. However, LDA is computationally expensive and may not perform well when dealing with short texts or single topic documents [15; 16]. Finally, one of the most widely used word-graph-based keyword extraction methods is TextRank [7]. This algorithm simulates the PageRank algorithm through a co-occurrence network, iteratively calculates the importance scores of the nodes, and extracts the words with higher scores as keywords. In recent years, researchers have proposed a number of extensions to TextRank, such as SW-TextRank and DK-TextRank, which incorporate semantic features and optimise word weights, but these methods often fail to fully balance multiple linguistic features [17; 18]. In recent years, researchers have proposed a variety of improved algorithms to improve the accuracy and adaptability of keyword extraction. Among the existing methods, NE-Rank and SemanticRank are typical representatives based on semantics and word weight optimisation. NE-Rank mainly enhances the importance score of words by word frequency, thereby improving the accuracy of keyword extraction. However, due to the lack of consideration of the semantic relationship between words, NE-Rank performs poorly when processing complex texts or data in specific fields [19]. SemanticRank introduces word vector technology to improve the accuracy of keyword extraction through semantic similarity [20]. However, its performance in specific domain data is limited because the semantic modelling of this method tends to be of general context [21]. In contrast, the IWF-TextRank proposed in this paper comprehensively considers multiple features such as word frequency, lexicality, word length, and semantics, and combines the BP neural network to optimise weights, which is more suitable for keyword extraction in specific fields. Table 1 compares the main features and performance of NE-Rank, SemanticRank, and IWF-TextRank to more clearly show the innovations and advantages of IWF-TextRank. The remainder of this paper is structured as follows: Section 2 describes the IWF-TextRank modelling framework and the methodology used in this study. Section 3 discusses the experimental results and analyses them in comparison with existing methods. Finally, Section 4 summarises the contributions of this paper and suggests potential directions for future research.