Millions of people post messages every day in social media net- works, especially on microblogging ones, like Twitter. There has been a major effort on monitoring all those messages for social media analytics to boost social media actions like marketing campaigns. Although there has been some approaches to detect and visualize topics trends of social media text analytics, this is an area full of challenges and open problems. We tackle in this poster the problem of visualizing real-time topics trends with sentiment analysis of streaming Twitter data from Brazilian users during games of the 2013 FIFA Confederations Cup. We compute the co-related matrix of terms occurrence to reduce the original terms matrix sparsity and therefore to select the most relevant topics associated to each player to be visualized through time series.