Hidden Trends in 90 Years of Harvard Business Review
This work provides insights into business and management trends over time, but it is incremental as it applies existing NLP methods to a new dataset.
The authors analyzed 90 years of Harvard Business Review abstracts using n-grams, sentiment analysis, and named-entity recognition to uncover trends in international relationships, sentiment, companies, inventions, researchers, and US presidents.
In this paper, we demonstrate and discuss results of our mining the abstracts of the publications in Harvard Business Review between 1922 and 2012. Techniques for computing n-grams, collocations, basic sentiment analysis, and named-entity recognition were employed to uncover trends hidden in the abstracts. We present findings about international relationships, sentiment in HBR's abstracts, important international companies, influential technological inventions, renown researchers in management theories, US presidents via chronological analyses.