TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	REMOVE
Sentiment Analysis	MR	MEAN	Accuracy	84.5	# 5
Sentiment Analysis	SST-5 Fine-grained classification	MEAN	Accuracy	51.4	# 15

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A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification

ACL 2018 · Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu ·

Deep learning approaches for sentiment classification do not fully exploit sentiment linguistic knowledge. In this paper, we propose a Multi-sentiment-resource Enhanced Attention Network (MEAN) to alleviate the problem by integrating three kinds of sentiment linguistic knowledge (e.g., sentiment lexicon, negation words, intensity words) into the deep neural network via attention mechanisms. By using various types of sentiment resources, MEAN utilizes sentiment-relevant information from different representation subspaces, which makes it more effective to capture the overall semantics of the sentiment, negation and intensity words for sentiment prediction. The experimental results demonstrate that MEAN has robust superiority over strong competitors.