TY - GEN
T1 - On Opinion Characterization in Social Sensing
T2 - 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018
AU - Zhang, Yang
AU - Vance, Nathan
AU - Zhang, Daniel Yue
AU - Wang, Dong
N1 - Funding Information:
ACKNOWLEDGMENT This material is based upon work supported by the National Science Foundation under Grant No. CBET-1637251, CNS-1566465 and IIS-1447795, Google Faculty Research Award 2017, and Army Research Office under Grant W911NF-17-1-0409. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/25
Y1 - 2018/10/25
N2 - Social sensing has emerged as a new application paradigm in networked sensing where data is collected from humans or devices on their behalf. This paper focuses on the opinion characterization problem in social sensing where the goal is to accurately characterize opinion attributes of the participants (e.g., analyze the sentiments, understand the opinion bias) from their sensor measurements. Several important challenges exist in solving the opinion characterization problem. First, human sensors often generate unstructured data (e.g., text, image, video) in which the opinion attributes are deeply embedded. Second, human sensors naturally generate measurements in different data modalities which encode the opinion attributes differently. Third, the possible imbalance between different data modalities may lead to potential bias in the opinion characterization results. To address the above challenges, this paper develops a Multi-View Opinion Characterization (MVOC) scheme to accurately characterize opinion attributes using a multi-view subspace learning approach. We evaluate the MVOC scheme through the real-world social sensing task of classifying the sentiments of reports from Twitter users. The evaluation results show that our scheme significantly outperforms the state-of-the-art baselines in solving the opinion characterization problem.
AB - Social sensing has emerged as a new application paradigm in networked sensing where data is collected from humans or devices on their behalf. This paper focuses on the opinion characterization problem in social sensing where the goal is to accurately characterize opinion attributes of the participants (e.g., analyze the sentiments, understand the opinion bias) from their sensor measurements. Several important challenges exist in solving the opinion characterization problem. First, human sensors often generate unstructured data (e.g., text, image, video) in which the opinion attributes are deeply embedded. Second, human sensors naturally generate measurements in different data modalities which encode the opinion attributes differently. Third, the possible imbalance between different data modalities may lead to potential bias in the opinion characterization results. To address the above challenges, this paper develops a Multi-View Opinion Characterization (MVOC) scheme to accurately characterize opinion attributes using a multi-view subspace learning approach. We evaluate the MVOC scheme through the real-world social sensing task of classifying the sentiments of reports from Twitter users. The evaluation results show that our scheme significantly outperforms the state-of-the-art baselines in solving the opinion characterization problem.
KW - Multi-View Learning
KW - Opinion Characterization
KW - Social Sensing
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85050250333&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050250333&partnerID=8YFLogxK
U2 - 10.1109/DCOSS.2018.00032
DO - 10.1109/DCOSS.2018.00032
M3 - Conference contribution
AN - SCOPUS:85050250333
T3 - Proceedings - 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018
SP - 155
EP - 162
BT - Proceedings - 14th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 June 2018 through 19 June 2018
ER -