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Sign of the Times: Sentiment Analysis on Historical Text and the Implications of Language Evolution

Sign of the Times: Sentiment Analysis on Historical Text and the Implications of Language Evolution
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Author(s): Tyler W. Soiferman (Stevens Institute of Technology, USA) and Paul J. Bracewell (DOT loves data, New Zealand)
Copyright: 2022
Pages: 16
Source title: Advanced Practical Approaches to Web Mining Techniques and Application
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Zdzislaw Polkowski (Wroclaw University of Economics, Poland) and Bharat Bhushan (Sharda University, India)
DOI: 10.4018/978-1-7998-9426-1.ch005

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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 This chapter demonstrates how language has evolved over time by exploring historical documents from the USA.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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