Using articles from the NY Times and Dynamic Topic Modelling to assess how the discussion of 'climate change' and 'global warming' have changed over time.
Inspired by https://bl.ocks.org/WillTurman/4631136
Brief methods: New York Times (NYT) articles were accessed through the NYT API using the search terms 'climate change' and 'global warming' for the years January 1988 through March 2016. After tokenizing the data, removing very common words, and words that only occurred a single time, a Dynamic Topic Modeling analysis was performed using the Python package 'gensim'. Eight different topics and the words most associated with those topics over time were identified. Visualizations of the topics themselves, and the words associated with them, were created to aid in identifying how the discussion of global climate has shifted. Dynamic Topic Modeling is an exploratory analysis tool, attempting to identify latent or hidden features in text data. As such the results should be viewed as suggesting areas of additional emphasis rather than any causal or diffinitive relationships. Additionally, these data were drawn from the New York Times. Text from other publications would likely show different, but likely interesting patterns.