Context-Aware Social Media User Sentiment Analysis

被引：0

作者：

Bo Liu ^{[1
,2
]}

Shijiao Tang ^{[1
,2
]}

Xiangguo Sun ^{[1
,2
]}

Qiaoyun Chen ^{[3
]}

Jiuxin Cao ^{[4
]}

Junzhou Luo ^{[1
,2
]}

Shanshan Zhao ^{[5
]}

机构：

[1] the School of Computer Science and Engineering, Southeast University

[2] the Key Laboratory of Computer Network and Information of Ministry of Education of China

[3] Microsoft Research Asia

[4] the School of Cyber Science and Engineering, Southeast University

[5] the Department of Computer Science and Creative Technologies, University of the West of England

来源：

Tsinghua Science and Technology | 2020年 / 25卷 / 04期

关键词：

social media; sentiment analysis; multimodal data; context-aware; topic model;

D O I：

暂无

中图分类号：

TP393.092 []; TP391.1 [文字信息处理];

学科分类号：

080402 ; 081203 ; 0835 ;

摘要：

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.

引用

页码：528 / 541

页数：14

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