Abstract-In this work, a software application was developed to analyze and visualize messages over Twitter social network, ranking the posts relatively to variations in moods within the Brazilian territory. Artificial intelligence techniques such as text mining and sentiment analysis were used for this purpose. The use of methods of machine learning allows determining the polarity (positive or negative) of tweets collected. Results were displayed in cartograms, through representations of tweet's geographic locations. Surprisingly, another study of twitter's mood from United States Nation showed similar results for the variation of moods throughout the day, hypothesizing a humor pattern for human beings during the period of 24 hours.
Abstract-Child labor is an issue of utmost importance to the world. According to the ILO (International Labour Organisation) nearby 218 million children between 5 and 17 work in the world, of which 50% holds hazardous work, The question rises up on how to locate and understand the factors about those families that generates the indices of child labor, and which properties are important to analyze. By the use of data mining techniques to discover valid patterns among Brazilian social data bases, we evaluate child labor in the federated state of Tocantins. This work has the purpose towards uncovering the deterministic factors for the practice of child labor and their relationships among financial, educational, cultural and social indicators; generating information that was not aware provided by data bases feeders, being hidden among records.Index Terms-Data mining, social data, child labor, welfare.
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