In our study, we investigate Twitter posts (tweets) related to climate change and global warming between January 2012 and March 2014. Using publicly available tweets and two monitors, one for tweets containing the hashtag #ClimateChange and another for tweets containing #GlobalWarming, we categorize tweets using automated opinion mining software provided by the social media monitoring company Crimson Hexagon. The software, ForSight, categorized tweets into one of seven categories. Tweets in each monitor were analyzed separately for additional comparison. In addition to the categorical totals over this time period, we examined these data over time using Pearson's correlations between volume of tweets and meteorological data. Meteorological data included temperature anomalies and variance, which were extracted from the Climate Forecast System Reanalysis and averaged over spatio-temporal regions in order to create a dataset with dimensions similar to that of the Twitter data. This comparison of time series revealed a number of interesting spikes in tweet volume tied to both meteorological events (e.g. heat waves, cold waves, large storms) as well as political events (e.g. elections, climate change conferences). Responses to both meteorological and political events in the #ClimateChange and #GlobalWarming monitors differ from each other, providing additional insight into public perception of and variance in usage of each term.