Opinion Mining on Offshore Wind Energy for Environmental Engineering

Renewable energy sources are vital to help mitigate the effects of climate change, and reducing the carbon dioxide emissions of fossil fuels, e.g. the state of New Jersey has a goal of producing 100% clean energy by 2050. However, the plans for offshore wind energy by the shore of the state still brings much controversy between residents due to the wind farms’ impact on wildlife, coastline, and the people’s view from the beaches. In this context, we perform sentiment analysis on social media data to investigate people’s opinions and concerns regarding offshore wind energy. We adapt 3 machine learning models, i.e. TextBlob, VADER and SentiWordNet for sentiment analysis because different functions are provided by each model, all of which are useful in our work. Techniques in NLP (natural language processing) are harnessed to gather meaning from the textual data in social media. Data visualization tools are suitably deployed to display the overall results. Despite the controversy surrounding this topic, our findings indicate some positive reception, suggesting potential support for modern-day renewable energy goals. However, there are neutral and negative comments as well, thus potentially helping to find areas for further improvement. The results of this work can be thus useful in a variety of decision-making contexts by governmental organizations and companies, hence aiding and enhancing offshore wind energy policy development. Hence, this work is much in line with citizen science and smart governance via involvement of mass opinion in decision support. In our paper, we highlight the role of sentiment analysis from social media in this aspect.