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Sign of the Times: Sentiment Analysis on Historical Text and the Implications of Language Evolution
Abstract
Natural language processing is a prevalent technique for scalably processing massive collections of documents. This branch of computer science is concerned with creating abstractions of text that summarize collections of documents in the same way humans can. This form of standardization means these summaries can be used operationally in machine learning models to describe or predict behavior in real or near real time as required. However, language evolves. This chapter demonstrates how language has evolved over time by exploring historical documents from the USA. Specifically, the change in emotion associated with key words can be aligned to major events. This research highlights the need to evaluate the stability of characteristics, including features engineered based on word elements when deploying operational models. This is an important issue to ensure that machine learning models constructed to summarize documents are monitored to ensure latent bias, or misinterpretation of outputs, is minimized.
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