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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26 January 2024Domestically, the theme of the week centred around monetary policy and inflation, with the SA Reserve Bank (SARB) making its first repo rate decision of the year on Thursday. Furthermore, Stats SA released both consumer and producer price inflation data for December. Globally, monetary policy was also important, with the European Central Bank (ECB),...
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