Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
Table 13: Results for AESC on Youtubean.
['[BOLD] Model', '[BOLD] Emb.', '[BOLD] Tagging F1 single', '[BOLD] Tagging F1 joint', '[BOLD] Classification F1 +', '[BOLD] Classification F1 −', '[BOLD] Classification F1 0']
6.2.3 Youtubean In particular, the baseline models seem to be unable to correctly classify negative aspects. For this dataset, we found out that Senna provides better results than CoreNLP with an average absolute gain of 3.94 F1-score, which was significant at p=2.5×10−4. Embeddings did not provide statistically significant differences. Similarly, binary features did not statistically contribute to the performance either.