TY - JOUR

T1 - Understanding Environmental Posts

T2 - Sentiment and Emotion Analysis of Social Media Data

AU - Amangeldi, Daniyar

AU - Usmanova, Aida

AU - Shamoi, Pakizar

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2024/3/7

Y1 - 2024/3/7

N2 - Social media is now the predominant source of information due to the availability of immediate public response. As a result, social media data has become a valuable resource for comprehending public sentiments. Studies have shown that it can amplify ideas and influence public sentiments. This study analyzes the public perception of climate change and the environment over a decade from 2014 to 2023. Using the Pointwise Mutual Information (PMI) algorithm, we identify sentiment and explore prevailing emotions expressed within environmental tweets across various social media platforms, namely Twitter, Reddit, and YouTube. Accuracy on a human-annotated dataset was 0.65, higher than Vader's score but lower than that of an expert rater (0.90). Our findings suggest that negative environmental tweets are far more common than positive or neutral ones. Climate change, air quality, emissions, plastic, and recycling are the most discussed topics on all social media platforms, highlighting its huge global concern. The most common emotions in environmental tweets are fear, trust, and anticipation, demonstrating the wide and complex nature of public reactions. By identifying patterns and trends in opinions related to the environment, we hope to provide insights that can help raise awareness regarding environmental issues, inform the development of interventions, and adapt further actions to meet environmental challenges.

AB - Social media is now the predominant source of information due to the availability of immediate public response. As a result, social media data has become a valuable resource for comprehending public sentiments. Studies have shown that it can amplify ideas and influence public sentiments. This study analyzes the public perception of climate change and the environment over a decade from 2014 to 2023. Using the Pointwise Mutual Information (PMI) algorithm, we identify sentiment and explore prevailing emotions expressed within environmental tweets across various social media platforms, namely Twitter, Reddit, and YouTube. Accuracy on a human-annotated dataset was 0.65, higher than Vader's score but lower than that of an expert rater (0.90). Our findings suggest that negative environmental tweets are far more common than positive or neutral ones. Climate change, air quality, emissions, plastic, and recycling are the most discussed topics on all social media platforms, highlighting its huge global concern. The most common emotions in environmental tweets are fear, trust, and anticipation, demonstrating the wide and complex nature of public reactions. By identifying patterns and trends in opinions related to the environment, we hope to provide insights that can help raise awareness regarding environmental issues, inform the development of interventions, and adapt further actions to meet environmental challenges.

KW - climate change

KW - emotion analysis

KW - global warming

KW - pointwise mutual information

KW - public perception

KW - Reddit

KW - Sentiment analysis

KW - social media

KW - Twitter

KW - YouTube

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85187023491&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2024.3371585

DO - 10.1109/ACCESS.2024.3371585

M3 - Journal articles

AN - SCOPUS:85187023491

VL - 12

SP - 33504

EP - 33523

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -