用自建词库自动识别说服性文本中的情绪关键词
Automated Detection and Analysis of Power Words in Persuasive Text Using Natural Language Processing
- 基于网络数据构建专用词库，用Python工具检测文本中的情绪词
- 在演讲、广告等文本中验证，发现情绪词显著提升读者参与度
- 适合内容创作、营销与政策传播者优化表达策略
情绪词是能引发强烈情感反应并显著影响读者行为的词汇，在营销、政治和激励写作等领域至关重要。本研究提出一种基于从网络来源收集的综合性数据集构建的定制词库，实现说服性文本中情绪词的自动化检测与分析。开发专用Python工具The Text Monger，用于识别和统计文本中情绪词的出现频率。通过分析小说片段、演讲稿和营销材料等多种数据集，旨在分类评估情绪词对情感倾向和读者参与度的影响。研究结果揭示了情绪词在不同领域中的有效性，为内容创作者、广告商及政策制定者提升信息传递与互动策略提供了实用参考。
原文摘要 · Abstract (English)
Power words are terms that evoke strong emotional responses and significantly influence readers' behavior, playing a crucial role in fields like marketing, politics, and motivational writing. This study proposes a methodology for the automated detection and analysis of power words in persuasive text using a custom lexicon created from a comprehensive dataset scraped from online sources. A specialized Python package, The Text Monger, is created and employed to identify the presence and frequency of power words within a given text. By analyzing diverse datasets, including fictional excerpts, speeches, and marketing materials,the aim is to classify and assess the impact of power words on sentiment and reader engagement. The findings provide valuable insights into the effectiveness of power words across various domains, offering practical applications for content creators, advertisers, and policymakers looking to enhance their messaging and engagement strategies.
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