# Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSA

arXiv: https://arxiv.org/abs/2406.16071 · HTML: https://mlsanity.com/paper/2406.16071/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 23 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Viable Dependency Parsing as Sequence Labeling](https://mlsanity.com/paper/1902.10505/) (2019)
- [Constituent Parsing as Sequence Labeling](https://mlsanity.com/paper/1810.08994/) (2018)
- [Parsing as Pretraining](https://mlsanity.com/paper/2002.01685/) (2020)
- [A Survey on Aspect-Based Sentiment Classification](https://mlsanity.com/paper/2203.14266/) (2022)
- [How to best use Syntax in Semantic Role Labelling](https://mlsanity.com/paper/1906.00266/) (2019)
- [Sequence Labeling Parsing by Learning Across Representations](https://mlsanity.com/paper/1907.01339/) (2019)
- [A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021](https://mlsanity.com/paper/2106.04216/) (2021)
- [4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees](https://mlsanity.com/paper/2310.14319/) (2023)
- [Experimenting with UD Adaptation of an Unsupervised Rule-based Approach for Sentiment Analysis of Mexican Tourist Texts](https://mlsanity.com/paper/2309.05312/) (2023)
- [How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis](https://mlsanity.com/paper/1706.02141/) (2017)
- [Hexatagging: Projective Dependency Parsing as Tagging](https://mlsanity.com/paper/2306.05477/) (2023)

## Built on by (most cited first, 2 of 2)

- [Evaluating Compositional Approaches for Focus and Sentiment Analysis](https://mlsanity.com/paper/2508.07810/) (2025)
- [Lost in Speech: Benchmarking, Evaluation, and Parsing of Spoken Bilingual Conversational Language Beyond Standard UD Assumptions](https://mlsanity.com/paper/2602.06307/) (2026)

## Abstract (arXiv)

Sentiment analysis is a key technology for companies and institutions to gauge public opinion on products, services or events. However, for large-scale sentiment analysis to be accessible to entities with modest computational resources, it needs to be performed in a resource-efficient way. While some efficient sentiment analysis systems exist, they tend to apply shallow heuristics, which do not take into account syntactic phenomena that can radically change sentiment. Conversely, alternatives that take syntax into account are computationally expensive. The SALSA project, funded by the European Research Council under a Proof-of-Concept Grant, aims to leverage recently-developed fast syntactic parsing techniques to build sentiment analysis systems that are lightweight and efficient, while still providing accuracy and explainability through the explicit use of syntax. We intend our approaches to be the backbone of a working product of interest for SMEs to use in production.