for same months in 2015 and 2016 where P.R. residents died of sepsis. The large increase could be explained by delayed medical treatment in homes and hospitals caused by Hurricane Maria damage.
U.S public tired of hearing about hurricanes? To what degree did the US public lack empathy or care for Puerto Rico because they didn’t view the territory as part of the U.S. (thus viewed it as foreign news which has been proven by many studies to have a short attention span)? Trump called PR beggars and stealers. A country with already poor infrastructure. To what extent did racism & class influence lack of empathy, otherness, take place in the lack of empathy for the situation?
in volume of NYT press coverage? Was coverage proportional to the catastrophic impacts? What was the sentiment during and 1 month following the hurricane? 1 year later?
more press coverage as the situation got worse in P.R? Was there more press coverage after more people died in P.R. over time? How did sentiment change over the course of a year after the hurricane ended?
During the start of hurricane, one month after, 1 year later Concurrent overlap mention in some articles Can come from different sections: sports, podcast, travel
rule-based sentiment analysis tool Trained on social media text, ideal for tweets based on lexicons of sentiment-related words. In this approach, each of the words in the lexicon is rated as to whether it is positive or negative, and in many cases, how positive or negative.
0.69 VADER produces four sentiment metrics from these word ratings. The first three, positive, neutral and negative, represent the proportion of the text that falls into those categories. The final metric, the compound score, is the sum of all of the lexicon ratings (1.9 and 1.8 in this case) which have been standardised to range between -1 and 1. In this case, our example sentence has a rating of 0.69, which is pretty strongly positive.
Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. • Silge, Julia & Robinson, David (2017).Tidy Text Mining with R. • Amber E. Boydstun. Making the News: Politics, the Media, and Agenda Setting. Chicago: University of Chicago Press. 2013.
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