Abstract
Sentiment analysis is a new, relatively unexplored, but potentially helpful method technical communicators can measure shifts in feeling, values, attitudes and beliefs, which drive behavior. Sentiment analysis is a relatively new method so there is little research in technical communications that address best practices. This poster demonstrates the process by which I mined sentiment from a corpus of unstructured text using a dictionary, categorization model and co-location algorithms. Preliminary results are consistent with empirical and historical observations, showing an uptick in negative emotion during moments of greatest political and scientific controversy. The implications of this research suggest new programs and greater access to digitized texts offer the practitioner and scholar new sites and tools with which to conduct research wider in scale and scope, but that the disambiguation of texts continues to prevent total automation of such processes.
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