Sentiment Analysis of Oil Company Annual Reports


A research paper I co-authored with Laura Muir, Associate Professor at the School of Computing Edinburgh Napier University has been published this week in the Journal of Knowledge Organization.

It is being increasingly recognized that sentiment analysis is a key part of enterprise search & discovery capability.

We applied sentiment analysis to public oil company annual reports. One company stands out for its over-positive rhetoric, the “Pollyanna Effect” towards the future, relative to its peers.

A lexicon was developed to detect edge member strong and hesitant forward looking language. Biologically inspired diversity algorithms were used to identify word patterns over time in companies, compared to subsequent revenue changes. One oil company showed a statistically significant association: their diversity of strong/hesitant language increased prior to a subsequent decrease in relative business performance.

A major industrial accident was also detected in another company’s reports without a need to read them. These were manifested through spike increases in the relative frequency of the topic ‘lessons’ followed by a spike in topics relating to the ‘future’. The effects of the catastrophe were still evident in word patterns several years after its occurrence. This supports the probable existence of Discourse of Renewal Theory (DRT) in practice.

The findings support the assertion that various social phenomena can be found in company reports by analysing word patterns over time – and some may have predictive properties. There may be benefits of applying sentiment algorithms (as standard) in enterprise search and discovery deployments.

Links here: Issue 2 KO and Institutional Repository



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