处方药的数字流行病学在X (以前的Twitter) 上的药物参考:神经网络主题建模和情绪分析
Varun K Rao1, Danny Valdez2, Rasika Muralidharan3
1Department of Epidemiology & Biostatistics, School of Public Health Bloomington, Indiana University Bloomington, Bloomington, IN, United States.
Journal of medical Internet research
|August 23, 2024
概括
下一代自然语言处理有效地区分了处方药街头和社交媒体上的品牌名称. 品牌讨论更连贯,情绪分析更可靠,突出了理解在线药物对话的上下文数据的重要性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 社交媒体分析
背景情况:
- 像X (以前的Twitter) 这样的社交媒体平台提供了对药物使用讨论的见解.
- 以前的方法 (隐性迪里克莱特分配) 由于语言模两可,难以对毒品街道名称进行分类.
- 品牌药物引用以前使用机器学习更容易分类.
研究的目的:
- 应用先进的自然语言处理 (NLP) 技术,超越隐藏的迪里克莱特分配 (LDA) 来分析X数据.
- 为了自动将推特集成到主题中,区分街头和品牌药物名称的提及.
- 为了比较街头和品牌药物讨论数据集之间的情绪价值和参与模式.
主要方法:
- 通过X API收集了包含处方药街头和品牌名称的推特.
- 使用BERTopic,统一的多重近似和投影,以及k-means用于在街道 (n=170,618) 和品牌 (n=245,145) 名称的主题建模.
- 使用Valence Aware词典和情感推理器 (VADER) 来进行情感分析和后勤回归模型,以使用参与度指标和推文文本来预测情绪.
主要成果:
- BERTopic确定了40个街名主题和5个品牌主题,分别概括为8个和5个主题.
- 与街道名称讨论相比,品牌讨论中更多的是与毒品相关的主题 (4),而不是街道名称讨论 (2).
- 两家公司都显示出积极的情绪偏见;结合推特文本,模型的准确性提高了40%左右.
结论:
- BERTopic有效地对推特进行了分类,品牌名称讨论比街道名称讨论更加连贯.
- 使用VADER的情绪分析更适用于品牌名称集体,因为主题更清晰.
- 单单是推特参与度不足以预测情绪;背景推特数据对于理解情绪至关重要.
关键词:
伯特论坛主题 伯特论坛主题在NLP中,我们使用了NLP.他们的推特是Twitter.瓦德尔 (VADER) 是一个伟大的人.瓦伦斯意识到字典和情感推理器.品牌名称 品牌名称 品牌名称数字流行病学数字流行病学毒品 毒品 毒品 是一种药物.使用毒品使用毒品.药物 药物 药物 药物 药物 药物医疗信息学 医疗信息学隐藏的迪里克莱特分配.逻辑回归的逻辑回归机器学习是机器学习.由机器驱动的机器驱动.自然语言处理自然语言处理.一个平台X个平台X个平台处方 处方是一种处方.处方药是指处方药的药物.医疗处方 处方 处方 处方情绪分析是一种情绪分析.社交媒体 社交媒体推文 推文 推文 推文推特发出一些推文.
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