Analysis of the feelings of the population’s opinion in social media: a look at education

This research presents a work in which we identify and systematize how the vertiginous growth of social media allows the monitoring of public opinions, with a special focus on analyzing the feelings of the population’s opinionated arguments about Education. We have brought together different methods in order to produce better results for the classification and summarization of various documents considering education as the basis of analysis. The proposed model is based on the steps of i) classification of patterns based on Deep Learning; ii) analysis of contexts and visualization of different associative paths in publications through the Implicative Statistical Analysis; and iii) validation of opinion abstracts. The results presented in this study refer to the database made up of 42,062 publications related to the city. The collective social discourses, resulting from the analysis of the summarize the opinions of 820 posts that presented representative terms for the education axis in the negative polarity, of the total of 975 posts classified by the dataset.

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