Word list as a baseline for measuring a classifier's performance? I am working on a simple Naive Bayes classifier that categorizes text messages as either &quot;positive&quot; or &quot;negative&quot;. I was told that the simplest baseline to measure the classifier's performance would be to compare it against a word list.
However, I have no clue as to how this might be helpful.
How can a word list be a baseline for a classifier?
P.S. I am new to machine learning and data science, so please don't hate on my question.
Features of your naive bayes classifier is most likely a bag of words where each feature represents a single word.
The naive bayes classifier also assumes that each feature (or word) contributes independently to the final prediction.
So if your sentence is composed of a single word - you get an idea of how the model reacts to that word.