一个基于NLP的两阶段框架,用于评估印度最高法院判决的情绪
Isha Gupta1, Indranath Chatterjee2, Neha Gupta1
1Faculty of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, 121003 India.
概括
本研究使用印度法律文件的主题建模和情绪分析来了解公众对法律问题的看法. 该研究确定了关键主题和公众情绪,为法律话语提供了新的见解.
科学领域:
- 计算语言学 计算语言学
- 法律信息学 法律信息学
- 社会科学研究 社会科学研究
背景情况:
- 主题建模揭示了大型数据集中的模式.
- 情绪分析衡量了公众对各种问题的看法.
- 了解公众对法律问题的看法至关重要.
研究的目的:
- 为分析印度法律文件提出一种新的两阶段自然语言处理模型.
- 确定关键的法律主题,并评估公众对这些主题的情绪.
- 探索法律话语对公众看法的影响.
主要方法:
- 利用隐性迪里克莱特分配 (LDA) 进行法律案件和判决的主题建模.
- 雇佣了Valence Aware字典情感推理器 (VADER) 用于对相关媒体的情感分析.
- 开发了一种两阶段的NLP方法,整合了主题和情绪分析.
主要成果:
- 成功地确定了印度法律文件中的关键主题.
- 与特定法律主题相关的量化公众情绪.
- 建立了一种分析法律文本和论的新方法.
结论:
- 这项研究开创了主题建模和情绪分析在印度法律领域的应用.
- 这些发现为了解公众对有争议的法律问题的看法提供了基础.
- 拟议的模型为法律信息学和社会法律研究提供了一个新的工具.
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