A Comparison of Lexicon-Based and ML-Based Sentiment Analysis: Are There
  Outlier Words?
Siddhant Jaydeep Mahajani Shashank Srivastava Alan F. Smeaton
Lexicon-based approaches to sentiment analysis of text are based on each word
or lexical entry having a pre-defined weight indicating its sentiment polarity.
These are usually manually assigned but the accuracy of these when compared
against machine leaning based approaches to computing sentiment, are not known.
It may be that there are lexical entries whose sentiment values cause a
lexicon-based approach to give results which are very different to a machine
learning approach. In this paper we compute sentiment for more than 150,000
English language texts drawn from 4 domains using the Hedonometer, a
lexicon-based technique and Azure, a contemporary machine-learning based
approach which is part of the Azure Cognitive Services family of APIs which is
easy to use. We model differences in sentiment scores between approaches for
documents in each domain using a regression and analyse the independent
variables (Hedonometer lexical entries) as indicators of each word's importance
and contribution to the score differences. Our findings are that the importance
of a word depends on the domain and there are no standout lexical entries which
systematically cause differences in sentiment scores.