The advantages of lexicon-based sentiment analysis in an age of machine learning.

We demonstrate the strong performance of lexicon-based sentiment analysis using MultiLexScaled, an approach which averages valences across a number of widely-used general-purpose lexica. We validate it against benchmark datasets from a range of different domains, comparing performance against machine learning and LLM alternatives. In addition, we illustrate the value of identifying fine-grained sentiment levels by showing, in an analysis of pre- and post- 9/11 British press coverage of Muslims, that binarized valence metrics give rise to different (and erroneous) conclusions about the nature of the post-9/11 shock as well as about differences between broadsheet and tabloid coverage. <br><br> __STAIR students over the years have been instrumental in helping test and develop the python notebooks we have put on Github to allow others to easily use the method.__ Check out Github to see more.

December 14, 2024

12:00 AM

PLOS One