Authors
Bo Liu, the School of Computer Science and Engineering, Southeast University, Nanjing 211189, China, and also with the Key Laboratory of Computer Network and Information of Ministry of Education of China, Nanjing 211189, China.
Shijiao Tang, the School of Computer Science and Engineering, Southeast University, Nanjing 211189, China, and also with the Key Laboratory of Computer Network and Information of Ministry of Education of China, Nanjing 211189, China.
Xiangguo Sun, the School of Computer Science and Engineering, Southeast University, Nanjing 211189, China, and also with the Key Laboratory of Computer Network and Information of Ministry of Education of China, Nanjing 211189, China.
Qiaoyun Chen, Microsoft Research Asia, Suzhou 215000, China.
Jiuxin Cao, the School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China.
Junzhou Luo, the School of Computer Science and Engineering, Southeast University, Nanjing 211189, China, and also with the Key Laboratory of Computer Network and Information of Ministry of Education of China, Nanjing 211189, China.
Shanshan Zhao, the Department of Computer Science and Creative Technologies, University of the West of England, Bristol, BS16 1QY, UK.
Keywords
social media, sentiment analysis, multimodal data, context-aware, topic model
Abstract
The user-generated social media messages usually contain considerable multimodal content. Such messages are usually short and lack explicit sentiment words. However, we can understand the sentiment associated with such messages by analyzing the context, which is essential to improve the sentiment analysis performance. Unfortunately, majority of the existing studies consider the impact of contextual information based on a single data model. In this study, we propose a novel model for performing context-aware user sentiment analysis. This model involves the semantic correlation of different modalities and the effects of tweet context information. Based on our experimental results obtained using the Twitter dataset, our approach is observed to outperform the other existing methods in analysing user sentiment.
Publisher
Tsinghua University Press
Recommended Citation
Bo Liu, Shijiao Tang, Xiangguo Sun et al. Context-Aware Social Media User Sentiment Analysis. Tsinghua Science and Technology 2020, 25(04): 528-541.
DOWNLOADS
Since October 30, 2020
COinS
