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Open AccessArticle

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

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Cyber Security INCT Unit 6, Laboratory for Decision-Making Technologies (LATITUDE), Department of Electrical Engineering (ENE), Faculty of Technology, University of Brasília (UnB), 70910-900 Brasília-DF, Brazil
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Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases 9, Ciudad Universitaria, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4557; https://doi.org/10.3390/s20164557
Received: 12 July 2020 / Revised: 5 August 2020 / Accepted: 6 August 2020 / Published: 14 August 2020
(This article belongs to the Special Issue Selected Papers from WISA 2020)
Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation. View Full-Text
Keywords: big data; botnet; monitoring; real-time visualization; sentiment analysis; text mining; social media; Twitter big data; botnet; monitoring; real-time visualization; sentiment analysis; text mining; social media; Twitter
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de Oliveira Júnior, G.A.; de Oliveira Albuquerque, R.; Borges de Andrade, C.A.; de Sousa, R.T., Jr.; Sandoval Orozco, A.L.; García Villalba, L.J. Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis. Sensors 2020, 20, 4557.

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