关于LSD和含有psilocybin的的经验报告
Ahmed Al-Imam1,2,3, Riccardo Lora4, Marek A Motyka5
1Department of Computer Science and Statistics, Poznan University of Medical Sciences, 61-806, Poznan, Poland. ahmed.al.imam@student.ump.edu.pl.
Drug safety
|March 3, 2025
概括
Erowid论坛数据的自然语言处理 (NLP) 显示了不同用户对和LSD的不同体验. 罗伯塔模型在分析这些复杂的迷幻报告时显示出高准确度,为减少危害的策略提供了信息.
科学领域:
- 计算语言学 计算语言学
- 心理药理学 心理药理学
- 数字人文学科 数字人文学科
背景情况:
- 迷幻药显示出治疗的前景,但在线论坛上的用户体验未得到充分分析.
- 欧罗伊德论坛的数据提供了关于迷幻药物的使用的丰富见解,但需要先进的分析方法.
- 自然语言处理 (NLP) 在分析主观迷幻体验中的应用是有限的.
研究的目的:
- 将NLP技术,包括情绪和词汇分析,应用于Erowid的psilocybin和LSD用户生成的报告.
- 量化和定性地描述这些迷幻物质的用户体验.
- 评估不同NLP模型在分析复杂的定性数据中的性能.
主要方法:
- 自动网络扫描来自Erowid的2188个用户报告 (psilocybin和LSD).
- 在经过大量文本预处理后,使用BERT,RoBERTa和VADER模型进行情绪分析.
- 词典分析通过n-gram识别和可视化识别关键主题.
主要成果:
- 在分类情绪方面,RoBERTa表现出了最高的准确性,超过了其他变压器模型.
- 词典分析揭示了不同的主题:psilocybin报告强调了内省和时间扩张;LSD报告强调了认知失调和记忆问题.
- 情绪分析显示了依赖模型的结果,VADER产生了两极分化的意见,RoBERTa提供了谨慎,准确的分类.
结论:
- NLP,特别是RoBERTa,有效地分析来自在线论坛的复杂迷幻体验.
- 识别了用户报告的不同主题,用于psilocybin和LSD体验.
- 研究结果支持NLP在理解迷幻效应,告知降低危害和指导政策方面的有用性.
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