With the averaging in place, we obtain three key findings: (1) the improvements in <cite>Hu et al. (2016)</cite> can almost entirely be attributed to just one of their two proposed mechanisms and are also less pronounced than previously reported; (2) contextualized word embeddings (Peters et al., 2018a) incorporate the "A-but-B" rules more effectively without explicitly programming for them; and (3) an analysis using crowdsourcing reveals a bigger picture where the errors in the automated systems have a striking correlation with the inherent sentiment-ambiguity in the data.