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 m...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.展开更多
基金supported by the National Key R&D Program of China(No.2017YFB1003000)the National Natural Science Foundation of China(Nos.61972087and 61772133)+4 种基金the National Social Science Foundation of China(No.19@ZH014)Jiangsu Provincial Key Project(No.BE2018706)the Natural Science Foundation of Jiangsu Province(No.SBK2019022870)Jiangsu Provincial Key Laboratory of Network and Information Security(No.BM2003201)Key Laboratory of Computer Network and Information Integration of Ministry of Education of China(No.93K-9).
文摘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.
