عنوان انگلیسی مقاله:
A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches
ترجمه فارسی عنوان مقاله:
تحقیق مشترک در زمینه اندازه گیری متن از ساختار لفاظی سخنان روسای جمهور ایالات متحده
Sciencedirect - Elsevier - Expert Systems With Applications, 123 (2019) 127-142: doi:10:1016/j:eswa:2018:12:049
Valerio Ficcadenti a , b , Roy Cerqueti a , ∗, Marcel Ausloos c , d
This work presents a text mining context and its use for a deep analysis of the messages delivered by 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 site 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 repro- ducible to a wide set of expert systems and text mining contexts. The usefulness of the proposed method and of the speeches 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.
Keywords: Text mining | Natural Language Processing | Politics | Rank-size laws