A machine learning approach to domain specific dictionary generation. An economic time series framework
Stellenbosch Working Paper Series No. WP06/2021Publication date: March 2021
Author(s):
This paper aims to offer an alternative to the manually labour intensive process of constructing a domain specific lexicon or dictionary through the operationalization of subjective information processing. This paper builds on current empirical literature by (a) constructing a domain specific dictionary for various economic confidence indices, (b) introducing a novel weighting schema of text tokens that account for time dependence; and (c) operationalising subjective information processing of text data using machine learning. The results show that sentiment indices constructed from machine generated dictionaries have a better fit with multiple indicators of economic activity than @loughran2011liability's manually constructed dictionary. Analysis shows a lower RMSE for the domain specific dictionaries in a five year holdout sample period from 2012 to 2017. The results also justify the time series weighting design used to overcome the p>>n problem, commonly found when working with economic time series and text data.
JEL Classification:C32, C45, C53, C55
Keywords:Sentometrics, Machine learning, Domain-specific dictionaries
Notes:Data download: Generated Dictionaries
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Monday 28 July 202512:00-13:00
Dr Neil Rankin: Ceo Of Predictive Insights & Stellenbosch University
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Topic: "Two competing approaches in South African competition policy: merger control and anti-cartel enforcement over the past 30 years"
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25 Jul 2025 Budget hurdle cleared, but US tariff implementation remains a riskIt was another big week on the local political front, but with some constructive momentum. On the trade front, ahead of next week’s 1 August deadline, Trump announced another “massive” trade deal with Japan. The upcoming week is busy, with a slew of global and domestic data releases and several monetary policy decisions, including the SARB....
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