Year: 2025
Type: article
Abstract: Here we present SENTimental, a simple and fast web-based, mobile-friendly tool for capturing sentiment annotations from participants and citizen scientist volunteers to create training and testing data for low-resource languages. In contrast to existing tools, we focus on assigning broad values to segments of text over specific tags for tokens or spans to build datasets for training and testing LLMs. The SENTimental interface minimises barriers to entry with a goal of maximising the time a user spends in a flow state whereby they are able to quickly and accurately rate each text fragment without being distracted by the complexity of the interface. Designed from the outset to handle multilingual representations, SENTimental allows for parallel corpus data to be presented to the user and switched between instantly for immediate comparison. As such this allows for users in any loaded languages to contribute to the data gathered, building up comparable rankings in a simple structured dataset for later processing.
Authors: Vidler, John, Rayson, Paul, Knight, Dawn
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: UK Research and Innovation
OpenAlex ID: W7081539008
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