Opinion Mining on Offshore Wind Energy for Environmental Engineering

TL;DR

This paper uses sentiment analysis on social media data with machine learning models to gauge public opinion on offshore wind energy, aiding environmental decision-making.

Contribution

It adapts and compares three NLP-based sentiment analysis models for offshore wind energy opinion mining, integrating visualization for insights.

Findings

Effective sentiment classification achieved with three models

Insights into public opinion trends on offshore wind energy

Supports citizen science and smart governance initiatives

Abstract

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.