We believe that this is mainly because SA@W and SWC are based on semantic and sentiment information at the word level, and these often capture a similar type of information.
for instance, disease names that are grouped together into one class in SWC are typically classified as negative in SA@W. the similarity in the information provided by SA@W and SWC causes a classifier trained with both of these features to obtain only a minor improvement over a classifier using only one of the features.