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

Carlos Gómez-Rodríguez, Muhammad Imran, David Vilares, Elena Solera, Olga Kellert
6/23/2024
cs.CL

No official code found. Best implementation: mirthAI/CIS-UNet (17 stars, MIT, last commit 2025-11-19).

Abstract

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.

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Code Implementations(1)

mirthAI/CIS-UNetCommunity100%
MIT
173Shell, PythonJul 12, 202411 months agoMIT

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Cite this paper

@article{gmezrodrguez2024dancing,
  title  = {Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSA},
  author = {Carlos Gómez-Rodríguez and Muhammad Imran and David Vilares and Elena Solera and Olga Kellert},
  year   = {2024},
  eprint = {2406.16071},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.16071v1}
}

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