Summary of Sentiment Analysis and Random Forest to Classify Llm Versus Human Source Applied to Scientific Texts, by Javier J. Sanchez-medina
Sentiment analysis and random forest to classify LLM versus human source applied to Scientific Texts
by Javier J. Sanchez-Medina
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 A new methodology is proposed for classifying texts generated by artificial intelligence (AI) or humans based on sentiment analysis features and random forest classification. The approach uses four different sentiment lexicons to produce novel features, which are then trained using a machine learning algorithm. This research line has promising implications for detecting fraud in environments where human-generated texts are expected. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence is making it possible to generate text automatically, but this raises questions about the future of education and academic procedures. Will many texts be written by machines instead of humans? To answer this, researchers have developed a way to classify texts as either AI-generated or human-written using sentiment analysis features and a machine learning algorithm. This method could help detect fraud in situations where human texts are expected. |
Keywords
* Artificial intelligence * Classification * Machine learning * Random forest
