Inteligência competitiva na cafeicultura: mineração textual em notícias publicadas na web

• Autor(a) principal: Lima Júnior, Paulo de Oliveira
• Data de Publicação: 2016
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/11844
• topic: Inteligência competitiva; Mineração textual; Mercado de café; Competitive intelligence; Text mining; Coffee market; Administração de Setores Específicos

Coffee production plays a significant role in Brazilian agribusiness. However, it is a high-risk activity given the impacts in different sectors of the production chain caused by coffee price variation. This demands Competitive Intelligence for the agents to monitor the competitive environment by means of a continuous and systematic process of information gathering and analysis for the decisionmaking in risk management. News with information that influence the coffee market and that affect the dynamics of its production chain are daily published online. However, dealing with the volume and speed of this information is not an easy task. It consumes human resources, time and restricts the capacity analysis of the specialists seeking and reading. The automation of the process, despite the advance in technology, meets obstacles in the syntactic field, such as residue on the data, and semantics, such as language ambiguity and absence of context. In this scenery, it was possible to acquire knowledge with the specialists regarding the intelligence for coffee production, by means of the iterative process of the Design Science Research method, and construct artifacts to automatically collect and classify, in the Competitive Intelligence perspective, internet news on events that influence the coffee market. A statistical evaluation showed correlation between the chronologic occurrence of these events and the price series and coffee volatility, while a qualitative evaluation made by specialist pointed the relevance of the news for analyzing the intelligence requisites in coffee production. These results point to the viability of an indicator for qualitative evidence derived from the internet, and its influence and error, while confronting the qualitative analysis, allowing us to perceive an increase in volatility and bias to design a decision-making scenery for risk management. Thus, this research corroborates the possibility of promoting Competitive Intelligence to support decisions regarding risk management and competitiveness in coffee production by means of Text Mining in news published in the internet