A machine learning approach to domain specific dictionary generation. An economic time series framework

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.

C32, C45, C53, C55

Sentometrics, Machine learning, Domain-specific dictionaries

Data download: Generated Dictionaries