Varieties of sentiment analysis and point pattern analysis are being applied to social media data to address a broad range of questions, but they are rarely used in tandem. This study outlines a methodology that combines these two approaches to analyze the spatial distribution of sentiment classified opinions f\rom social media data. Twitter postings on natural gas were downloaded and classified using a variety of sentiment analysis methods into positive, negative, and neutral categories. The classifications were then converted into spatial points using the location data associated with the tweets, whereby point pattern analysis techniques were applied to the points to examine the patterns of positive and negative tweet locations with respect to a background rate of neutral tweets across the contiguous Unites States. Basic temporal visualizations were also constructed to explore the variations in sentiment over time. Considerations are discussed on the accuracy limitations of sentiment analysis and the potential for a variety of applications using these techniques. With careful implementation, this methodology can open the door to a range of spatiotemporal analyses of social media sentiment.
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