Opinion Mining on Offshore Wind Energy for Environmental Engineering
This work addresses the need for incorporating mass opinion into decision support for offshore wind energy projects, but it is incremental as it applies standard NLP methods without novel advancements.
The paper tackled the problem of understanding public opinion on offshore wind energy by performing sentiment analysis on social media data using three existing machine learning models (TextBlob, VADER, and SentiWordNet), with results visualized to support decision-making in environmental engineering.
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.