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Twitter analytics for the study of primary emotions during the earthquake in Mexico in 2017

Abstract:

Emergency management uses Twitter as a valuable tool because it provides data on location, time, content and message propagation. This work analyzed 59416 tweets from the Mexican earthquake organized in two datasets: DSMI, tweets issued by private users through the label #terremoto and DCMI, tweets that mention emergency agencies during the earthquake. The purpose is to identify the expressed primary emotions, their evolution over time, and the propagation patterns by comparing the two datasets. The analysis was done with data mining techniques. It is concluded that emotions are expressed more widely in DSMI than in DCMI. The emotion that predominates is the anger and it is transformed with time, in DSMI it has a decreasing course while in DCMI it shows peaks of messages. The most propagated messages independently of the dataset are those that express anger and joy, polarized emotions.