Document scaling has been a key component in text-as-data applications for social scientists and a major field of interest for political researchers, who aim at uncovering differences between speakers or parties with the help of different probabilistic and non-probabilistic approaches. Yet, most of these techniques are either built upon the agnostically bag-of-word hypothesis or use prior information borrowed from external sources that might embed the results with a significant bias. If the corpus has long been considered as a collection of documents, it can also be seen as a dense network of connected words whose structure could be clustered to differentiate independent groups of words, based on their co-occurrences in documents, known as communities. This paper introduces CommunityFish as an augmented version of Wordfish based on a hierarchical clustering, namely the Louvain algorithm, on the word space to yield communities as semantic and independent n-grams emerging from the corpus and use them as an input to Wordfish method, instead of considering the word space. This strategy emphasizes the interpretability of the results, since communities have a non-overlapping structure, hence a crucial informative power in discriminating parties or speakers, in addition to allowing a faster execution of the Poisson scaling model. Aside from yielding communities, assumed to be subtopic proxies, the application of this technique outperforms the classic Wordfish model by highlighting historical developments in the U.S. State of the Union addresses and was found to replicate the prevailing political stance in Germany when using the corpus of parties' legislative manifestos.

该研究介绍了CommunityFish，它是Wordfish的一个增强版本，使用层次聚类方法将语料库中的词语空间聚类为语义和独立的n-grams社群，并将其作为Wordfish方法的输入，以强调结果的可解释性和提高执行速度。通过突出美国国情咨文的历史发展，并使用政党立法宣言的语料库，该技术不仅生成了社群，还能够复制德国的主导政治立场。

CommunityFish：基于泊松分布的具有层次聚类的文档缩放