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

arXiv: https://arxiv.org/abs/1807.04990 · HTML: https://mlsanity.com/paper/1807.04990/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction](https://mlsanity.com/paper/2305.12627/) (2023)
- [Iterative Document-level Information Extraction via Imitation Learning](https://mlsanity.com/paper/2210.06600/) (2022)
- [Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis](https://mlsanity.com/paper/2201.10274/) (2022)
- [KESA: A Knowledge Enhanced Approach For Sentiment Analysis](https://mlsanity.com/paper/2202.12093/) (2022)

## Abstract (arXiv)

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.