Keyword extraction refers to the process of detecting the most relevant terms and expressions in a given text in a timely manner. In the information explosion era, keyword extraction has attracted increasing attention. The importance of keyword extraction in text summarization, text comparisons, and document categorization has led to an emphasis on graph-based keyword extraction techniques because they can capture more structural information compared to other classic text analysis methods. In this paper, we propose a simple unsupervised text mining approach that aims to extract a set of keywords from a given text and analyze its topic diversity using graph analysis tools. Initially, the text is represented as a directed graph using synonym relationships. Then, community detection and other measures are used to identify keywords in the text. The set of extracted keywords is used to assess topic diversity within the text and analyze its sentiment. The proposed approach relies on grouping semantically similar candidate words. This approach ensures that the set of extracted keywords is comprehensive. Differing from other graph-based keyword extraction approaches, the proposed method does not require user parameters during graph construction and word scoring. The proposed approach achieved significant results compared to other keyword extraction techniques.

中文翻译：

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用于文本分析的单词同义词关系：一种基于图形的方法。

关键字提取是指及时检测给定文本中最相关的术语和表达的过程。在信息爆炸时代，关键词提取越来越受到关注。关键字提取在文本摘要、文本比较和文档分类中的重要性导致人们对基于图的关键字提取技术的重视，因为与其他经典文本分析方法相比，它们可以捕获更多的结构信息。在本文中，我们提出了一种简单的无监督文本挖掘方法，旨在从给定文本中提取一组关键字并使用图形分析工具分析其主题多样性。最初，文本使用同义词关系表示为有向图。然后，使用社区检测和其他措施来识别文本中的关键字。提取的关键字集用于评估文本中的主题多样性并分析其情绪。所提出的方法依赖于对语义相似的候选词进行分组。这种方法确保提取的关键字集是全面的。与其他基于图的关键字提取方法不同，所提出的方法在图构建和单词评分过程中不需要用户参数。与其他关键字提取技术相比，所提出的方法取得了显着的成果。所提出的方法在图形构建和单词评分期间不需要用户参数。与其他关键字提取技术相比，所提出的方法取得了显着的成果。所提出的方法在图形构建和单词评分期间不需要用户参数。与其他关键字提取技术相比，所提出的方法取得了显着的成果。