This paper employed Python-based natural language processing techniques to analyze social media data related to climate change, carbon neutrality, and single-use plastics, aiming to systematically understand the public's emotional responses to environmental issues and public opinion trends. After collecting a total of 30,000 tweets, text preprocessing and sentiment analysis were performed. Based on these results, qualitative and quantitative analyses were conducted through word cloud visualization and yearly sentiment distribution graphs.

 As a result of the analysis, the number of climate-related tweets has increased dramatically since 2022. In 2023~2024, posts containing negative emotions accounted for the highest proportion. This suggests that the climate disasters, international policy discussions, and social conflicts during the period had a strong impact on online public opinion. In the word cloud by emotion, words such as 'fossil fuel', 'government’, ‘crisis, and 'money' appeared repeatedly along with negative sentiment. On the other hand, 'clean energy', 'solution', and 'hope' were associated with positive emotions. Such analyses help visualize the main concerns of climate discourse and identify the emotional structure of public perception.

Keywords

Climate change, Carbon neutrality, Single-use plastics and Sentiment analysis