We first propose a new version of the Word Embedding Association Tests studied in <cite>[3]</cite> by using the LIWC lexica to systematically detect and measure the biases within the embedding, keeping the tests comparable with the same set of target words. We further extend this work using additional sets of target words, and compare sentiment across male and female names. Furthermore, we investigate gender bias in words that represent different occupations, comparing these associations with UK national employment statistics.