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

TL;DR

The paper introduces SALSA, a sentiment analysis system that combines fast syntactic parsing with efficiency and explainability, making it suitable for resource-constrained environments and small to medium enterprises.

Contribution

It presents a novel approach that leverages recent fast syntactic parsing techniques to create lightweight, accurate, and explainable sentiment analysis systems.

Findings

Achieves a balance between speed and accuracy in sentiment analysis.

Provides explainability through explicit syntactic analysis.

Designed for deployment in resource-limited settings.

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.