Summary of Using Llms to Establish Implicit User Sentiment Of Software Desirability, by Sherri Weitl-harms et al.
Using LLMs to Establish Implicit User Sentiment of Software Desirability
by Sherri Weitl-Harms, John D. Hastings, Jonah Lum
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study leverages large language models (LLMs) to provide quantitative zero-shot sentiment analysis for implicit software desirability, tackling the crucial challenge in product evaluation where traditional review scores lack depth. The proposed method works with qualitative user experience data without explicit review scores, focuses on implicit user satisfaction, and offers scaled numerical sentiment analysis, providing a more comprehensive understanding of user sentiment beyond simple positive, neutral, or negative classifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses big language models to understand how people feel about software without asking them directly. It’s hard to know what people really think just by looking at numbers like 1-5 stars. This research helps solve that problem by using the language model to analyze what people are saying online, and turn it into a number that shows how happy or unhappy they are with the software. |
Keywords
» Artificial intelligence » Language model » Zero shot
