TY - GEN
T1 - Identification of landscape preferences by using social media analysis
AU - Rai, Ankit
AU - Minsker, Barbara
AU - Diesner, Jana
AU - Karahalios, Karrie
AU - Sun, Yicheng
N1 - Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. ACI-1261582.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/25
Y1 - 2018/5/25
N2 - People attribute different values to landscapes. These values are due to people's preferences, which are shaped by aesthetics, recreational benefits, safety, and other features of landscapes. Classic methods for studying landscape preferences include surveys and questionnaires, where study participants score or evaluate photos. Since almost 70% of US adults use social media to connect with friends and families, or to follow news and topics of interest (Pew research, 2015), research is needed to identify whether social media postings provide useful information about preferences for landscape settings. In this paper, we label text comments from Flickr, Instagram, and Twitter to train a lexicon-based sentiment classification model for predicting people's sentiments about green infrastructures, which are a specific landscape element. The results show a 77% correlation between text based sentiments from social media and image based landscape preferences.
AB - People attribute different values to landscapes. These values are due to people's preferences, which are shaped by aesthetics, recreational benefits, safety, and other features of landscapes. Classic methods for studying landscape preferences include surveys and questionnaires, where study participants score or evaluate photos. Since almost 70% of US adults use social media to connect with friends and families, or to follow news and topics of interest (Pew research, 2015), research is needed to identify whether social media postings provide useful information about preferences for landscape settings. In this paper, we label text comments from Flickr, Instagram, and Twitter to train a lexicon-based sentiment classification model for predicting people's sentiments about green infrastructures, which are a specific landscape element. The results show a 77% correlation between text based sentiments from social media and image based landscape preferences.
KW - Crowd sourcing
KW - Green infrastructure
KW - Landscape preference
KW - Machine learning
KW - Sentiment analysis
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85048462633&partnerID=8YFLogxK
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U2 - 10.1109/SocialSens.2018.00021
DO - 10.1109/SocialSens.2018.00021
M3 - Conference contribution
AN - SCOPUS:85048462633
T3 - Proceedings - 3rd International Workshop on Social Sensing, SocialSens 2018
SP - 44
EP - 49
BT - Proceedings - 3rd International Workshop on Social Sensing, SocialSens 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Workshop on Social Sensing, SocialSens 2018
Y2 - 17 April 2018
ER -