揭示影响判断变化的因素在情绪分析中使用自然语言处理和统计数据进行情绪分析
Olga Kellert1, Carlos Gómez-Rodríguez1, Mahmud Uz Zaman2
1CITIC, Grupo LyS, Depto. de Ciencias de la Computación y Tecnologías de la Información, Universidade da Coruña, A Coruña, Spain.
PloS one
|May 31, 2024
概括
这项针对西班牙TripAdvisor评论的研究发现,单词标题中的形容词和情感词减少了极性判断的变化. 然而,中性词语增加了变化,在更长的标题中,效果会减少.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 情绪分析 情绪分析
背景情况:
- 众包数据,如TripAdvisor评论,对于NLP任务,特别是情绪分析至关重要.
- 了解影响西班牙审查中极性判断的因素对于准确的NLP应用至关重要.
研究的目的:
- 为了调查影响西班牙TripAdvisor评论中极性判断变化的因素.
- 测试语音部分 (POS),情感词和中立词对判断变化的影响.
主要方法:
- 来自西班牙语TripAdvisor评论的单词标题的分析.
- 关于不同词组 (POS,情绪,中立) 的平均平等的统计测试.
- 从单词标题与较长标题的结果进行比较.
主要成果:
- 一个单词标题中的形容词和情感词显著减少极性判断变化.
- 中立词与更高的判断变化有关.
- 由于上下文的影响,观察到的效应在较长的标题中不太一致.
结论:
- 一个单词的标题是有效的研究单独的单词极性,而更长的标题引入混因素.
- 结果为NLP从业者和研究人员提供了洞察力,他们致力于西班牙语情绪分析.
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