Summary of What Is Sentiment Meant to Mean to Language Models?, by Michael Burnham
What is Sentiment Meant to Mean to Language Models?
by Michael Burnham
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers explore the complexities of sentiment analysis in text analysis, specifically examining how large language models classify text based on prompts. The study reveals that sentiment labels are often strongly correlated with valence labels, suggesting that emotional tone is a primary factor. To improve classification accuracy, the authors recommend specifying a precise measurement construct instead of relying on the ambiguous concept of sentiment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper investigates how language models determine sentiment in text analysis. Sentiment can refer to various concepts like emotion or opinion, making it unclear what’s being measured. The study uses three language models and two datasets to analyze their performance with prompts asking for sentiment, valence, and stance classification. Results show that using more precise labels leads to better accuracy. Overall, this research encourages researchers to move beyond the vague concept of sentiment and use more specific measurement constructs. |
Keywords
» Artificial intelligence » Classification
