Summary of Opinion Mining on Offshore Wind Energy For Environmental Engineering, by Isabele Bittencourt et al.
Opinion Mining on Offshore Wind Energy for Environmental Engineering
by Isabele Bittencourt, Aparna S. Varde, Pankaj Lal
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel machine learning-based approach for sentiment analysis on social media data is proposed to study public opinion about offshore wind energy. The study leverages three models, TextBlob, VADER, and SentiWordNet, each offering unique functions such as subjectivity analysis, polarity classification, cumulative sentiment scores, and context-dependent sentiment classification. By harnessing natural language processing (NLP) techniques, the study extracts meaning from social media textual data and visualizes the results using suitable tools. This work aligns with citizen science and smart governance principles by involving public opinion in decision-making processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offshore wind energy is a hot topic on social media! Researchers used special computer programs to understand what people are really saying about it online. They looked at three different types of programs, each doing things like measuring how positive or negative the messages are. The programs helped them make sense of all the text data and show the results in a way that’s easy to understand. This study is important because it shows how ordinary people can be involved in making decisions about energy by sharing their opinions online. |
Keywords
* Artificial intelligence * Classification * Machine learning * Natural language processing * Nlp
