TY - GEN
T1 - Correlating Environmental Facts and Social Media Trends Leveraging Commonsense Reasoning and Human Sentiments
AU - McNamee, Brad
AU - Varde, Aparna S.
AU - Razniewski, Simon
N1 - Funding Information:
Aparna Varde acknowledges the NSF grants 2018575 (MRI: Acquisition of a High-Performance GPU Cluster for Research & Education); and 2117308 (MRI: Acquisition of a Multimodal Collaborative Robot System (MCROS) to Support Cross-Disciplinary Human-Centered Research & Education). She is a visiting researcher at Max Planck Institute for Informatics, Saarbrucken, Germany.
Publisher Copyright:
© European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
PY - 2022
Y1 - 2022
N2 - As climate change alters the physical world we inhabit, opinions surrounding this hot-button issue continue to fluctuate. This is apparent on social media, particularly Twitter. In this paper, we explore concrete climate change data concerning the Air Quality Index (AQI), and its relationship to tweets. We incorporate commonsense connotations for appeal to the masses. Earlier work focuses primarily on accuracy and performance of sentiment analysis tools/models, much geared towards experts. We present commonsense interpretations of results, such that they are not impervious to the masses. Moreover, our study uses real data on multiple environmental quantities comprising AQI. We address human sentiments gathered from linked data on hashtagged tweets with geolocations. Tweets are analyzed using VADER, subtly entailing commonsense reasoning. Interestingly, correlations between climate change tweets and air quality data vary not only based upon the year, but also the specific environmental quantity. We anticipate that this study will shed light on possible areas to increase awareness of climate change, and methods to address it, by the scientists as well as the common public. In line with Linked Data initiatives, we aim to make this work openly accessible on a network, published with the Creative Commons license.
AB - As climate change alters the physical world we inhabit, opinions surrounding this hot-button issue continue to fluctuate. This is apparent on social media, particularly Twitter. In this paper, we explore concrete climate change data concerning the Air Quality Index (AQI), and its relationship to tweets. We incorporate commonsense connotations for appeal to the masses. Earlier work focuses primarily on accuracy and performance of sentiment analysis tools/models, much geared towards experts. We present commonsense interpretations of results, such that they are not impervious to the masses. Moreover, our study uses real data on multiple environmental quantities comprising AQI. We address human sentiments gathered from linked data on hashtagged tweets with geolocations. Tweets are analyzed using VADER, subtly entailing commonsense reasoning. Interestingly, correlations between climate change tweets and air quality data vary not only based upon the year, but also the specific environmental quantity. We anticipate that this study will shed light on possible areas to increase awareness of climate change, and methods to address it, by the scientists as well as the common public. In line with Linked Data initiatives, we aim to make this work openly accessible on a network, published with the Creative Commons license.
KW - AQI
KW - Commonsense Reasoning
KW - Human Sentiments
KW - Linked Data
KW - Opinion Mining
KW - Twitter Hashtags
UR - http://www.scopus.com/inward/record.url?scp=85146226999&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85146226999
T3 - 2nd Workshop on Sentiment Analysis and Linguistic Linked Data, SALLD 2022 - held in conjunction with the International Conference on Language Resources and Evaluation, LREC 2022 - Proceedings
SP - 25
EP - 30
BT - 2nd Workshop on Sentiment Analysis and Linguistic Linked Data, SALLD 2022 - held in conjunction with the International Conference on Language Resources and Evaluation, LREC 2022 - Proceedings
A2 - Kernerman, Ilan
A2 - Carvalho, Sara
A2 - Iglesias, Carlos A.
A2 - Sprugnoli, Rachele
PB - European Language Resources Association (ELRA)
T2 - 2nd Workshop on Sentiment Analysis and Linguistic Linked Data, SALLD 2022
Y2 - 24 June 2022
ER -
