2019/01/04 by Valerio Ficcadenti, Roy Cerqueti, Marcel Ausloos · 33 citations
Computer Science · Mathematics · Physics and Astronomy · Psychology · Social Sciences · #Artificial intelligence #Authorship Attribution and Profiling #Computational and Text Analysis Methods #Computer science #Context (archaeology) #Data science #Flexibility (engineering) #Generality #History #Information retrieval #Law #Linguistics #Mathematics #Natural language processing #Perspective (graphical) #Political science #Politics #Psychology #Rank (graph theory) #Rhetoric #Sentiment Analysis and Opinion Mining #Set (abstract data type) #Statistics #cs.CL #physics.soc-ph
paper · pdf · doi:10.1016/j.eswa.2018.12.049
published in Expert Systems with Applications 123, 127-142 (Elsevier BV) · prepared for Expert Systems With Applications; 24 figures; 7 tables; 89 references
openalex publication_date 2019/01/04 · arxiv created 2019/05/09 · arxiv updated 2019/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This work presents a text mining context and its use for a deep analysis of the messages delivered by the politicians. Specifically, we deal with an expert systems-based exploration of the rhetoric dynamics of a large collection of US Presidents' speeches, ranging from Washington to Trump. In particular, speeches are viewed as complex expert systems whose structures can be effectively analyzed through rank-size laws. The methodological contribution of the paper is twofold. First, we develop a text mining-based procedure for the construction of the dataset by using a web scraping routine on the Miller Center website -- the repository collecting the speeches. Second, we explore the implicit structure of the discourse data by implementing a rank-size procedure over the individual speeches, being the words of each speech ranked in terms of their frequencies. The scientific significance of the proposed combination of text-mining and rank-size approaches can be found in its flexibility and generality, which let it be reproducible to a wide set of expert systems and text mining contexts. The usefulness of the proposed method and the speech subsequent analysis is demonstrated by the findings themselves. Indeed, in terms of impact, it is worth noting that interesting conclusions of social, political and linguistic nature on how 45 United States Presidents, from April 30, 1789 till February 28, 2017 delivered political messages can be carried out. Indeed, the proposed analysis shows some remarkable regularities, not only inside a given speech, but also among different speeches. Moreover, under a purely methodological perspective, the presented contribution suggests possible ways of generating a linguistic decision-making algorithm.