用AI分析社交媒体舆论，为海上风电政策提供民情参考。
Opinion Mining on Offshore Wind Energy for Environmental Engineering
- 融合TextBlob、VADER、SentiWordNet三模型进行情感分析
- 通过文本挖掘识别公众对海上风电的总体态度倾向
- 适合环境工程与智能治理研究者参考
本文通过社交媒体数据开展情感分析，研究公众对海上风电的舆论。采用TextBlob、VADER和SentiWordNet三种机器学习模型，分别实现主观性判断、综合情感评分与上下文感知分类。结合自然语言处理技术从社交媒体文本中提取语义信息，并利用数据可视化工具呈现整体结果。该研究契合公民科学与智慧治理理念，体现机器学习与NLP在公共决策支持中的应用价值。
原文摘要 · Abstract (English)
In this paper, we conduct sentiment analysis on social media data to study mass opinion about offshore wind energy. We adapt three machine learning models, namely, TextBlob, VADER, and SentiWordNet because different functions are provided by each model. TextBlob provides subjectivity analysis as well as polarity classification. VADER offers cumulative sentiment scores. SentiWordNet considers sentiments with reference to context and performs classification accordingly. Techniques in NLP are harnessed to gather meaning from the textual data in social media. Data visualization tools are suitably deployed to display the overall results. This work is much in line with citizen science and smart governance via involvement of mass opinion to guide decision support. It exemplifies the role of Machine Learning and NLP here.
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