Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
Table 11: Results for AESC on Laptops
['[BOLD] Model', '[BOLD] Emb.', '[BOLD] Tagging F1 single', '[BOLD] Tagging F1 joint', '[BOLD] Classification F1 +', '[BOLD] Classification F1 −', '[BOLD] Classification F1 0']
Based on the results for AE, we only used CoreNLP as a pre-processing pipeline. For the RNN baseline, embeddings also reported significant differences, with SennaEmbeddings offering average absolute gains of 5.78 F1-score (p=10−4) over GoogleNews and 2.47 F1-score (p=8×10−3) over WikiDeps.