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Document scaling has been a key component in text-as-data applications forsocial scientists and a major field of interest for political researchers, whoaim at uncovering differences between speakers or parties with the help ofdifferent probabilistic and non-probabilistic approaches. Yet, most of thesetechniques are either built upon the agnostically bag-of-word hypothesis or useprior information borrowed from external sources that might embed the resultswith a significant bias. If the corpus has long been considered as a collectionof documents, it can also be seen as a dense network of connected words whosestructure could be clustered to differentiate independent groups of words,based on their co-occurrences in documents, known as communities. This paperintroduces CommunityFish as an augmented version of Wordfish based on ahierarchical clustering, namely the Louvain algorithm, on the word space toyield communities as semantic and independent n-grams emerging from the corpusand use them as an input to Wordfish method, instead of considering the wordspace. This strategy emphasizes the interpretability of the results, sincecommunities have a non-overlapping structure, hence a crucial informative powerin discriminating parties or speakers, in addition to allowing a fasterexecution of the Poisson scaling model. Aside from yielding communities,assumed to be subtopic proxies, the application of this technique outperformsthe classic Wordfish model by highlighting historical developments in the U.S.State of the Union addresses and was found to replicate the prevailingpolitical stance in Germany when using the corpus of parties' legislativemanifestos.

CommunityFish: A Poisson-based Document Scaling With Hierarchical Clustering