Access to Information in Brazil and Data Science: Descendent Hierarchy Classification on requests made to São Paulo municipality from 2012 to 2019
classificação hierárquica descendente em pedidos realizados à Prefeitura de São Paulo de 2012 a 2019
DOI:
https://doi.org/10.36428/revistadacgu.v14i26.544Keywords:
access to information, access to information requests, computer text analysis, descending hierarchical classification, topic classificationAbstract
This article sought to understand how Data Science, text mining and text classification technologies can contribute to a better, aggregated understanding of access to information requests. Research used data from access to information requests made to the Municipality of São Paulo (PMSP), from 2012 to 2019, available on the municipality's Open Data Portal, proposing presented main issues identification and classification. 39,369 request texts submitted to PMSP were gathered into a corpus and submitted to analysis through a Descending Hierarchical Classification (CHD). In this same corpus, five demographic variables were inserted for each request, which were submitted to a standard text pre-processing routine, selecting 31,946 for analysis by CHD (81,16%). By proposing a classification of texts as a methodology for analyzing textual data, a paradigm was reinforced that textual data do not belong only to the qualitative field. In addition, considering only nouns, excluding verbs and adverbs, and the most frequent adjectives being used as part of vocabulary expressions, allowed a request context optimization, allowing to classify the textual data in a more objective way, mitigating the researchers' bias. Seven classes resulted from the analysis through the Descending Hierarchical Classification: 1 - Neighborhoods and districts; 2 - Procedure and procedural documents; 3 - Public contracts; 4 - Urban mobility; 5 - Family: health, education and social assistance; 6 - Real estate; and 7 - Public tenders and positions (workers). We present main references found in access to information requests analysis, cases in Mexico, national studies, and in China, in the city of Beijing, and contributes to the understanding citizens requests in an aggregated way, allowing decision makers to a better understanding of society's demands, which may result in more focused public policies.
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