# Latent Variable Sentiment Grammar

arXiv: https://arxiv.org/abs/1907.00218 · HTML: https://mlsanity.com/paper/1907.00218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Interpreting Sentiment Composition with Latent Semantic Tree](https://mlsanity.com/paper/2308.16588/) (2023)

## Abstract (arXiv)

Neural models have been investigated for sentiment classification over constituent trees. They learn phrase composition automatically by encoding tree structures but do not explicitly model sentiment composition, which requires to encode sentiment class labels. To this end, we investigate two formalisms with deep sentiment representations that capture sentiment subtype expressions by latent variables and Gaussian mixture vectors, respectively. Experiments on Stanford Sentiment Treebank (SST) show the effectiveness of sentiment grammar over vanilla neural encoders. Using ELMo embeddings, our method gives the best results on this benchmark.