Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSA
This work addresses the need for accessible sentiment analysis tools for small and medium-sized enterprises (SMEs) to gauge public opinion, though it appears incremental as it builds on existing parsing methods.
The paper tackles the problem of making large-scale sentiment analysis resource-efficient for entities with modest computational resources by leveraging fast syntactic parsing techniques, aiming to build lightweight systems that maintain accuracy and explainability.
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.