当前位置: X-MOL 学术Comput. Environ. Urban Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Domain-specific sentiment analysis for tweets during hurricanes (DSSA-H): A domain-adversarial neural-network-based approach
Computers, Environment and Urban Systems ( IF 6.454 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.compenvurbsys.2020.101522
Fang Yao , Yan Wang

Abstract Hurricanes are one of the most frequent and destructive disasters in the United States. The events are large scale and have relatively long-term impacts. Social networking platforms such as Twitter can provide real-time information for disaster managers and affected populations during large-scale disasters (e.g., hurricanes), but extracting useful information and interpreting data accurately for disaster management is still challenging. Sentiment analysis of social media data helps detect the concerns of affected people and understand individuals' responses on the ground at unprecedented scales, but the method is known to be domain-dependent. The same words or expressions can indicate opposite sentiments in different domains. This paper proposes a domain-specific sentiment analysis approach specifically for tweets posted during hurricanes (DSSA-H). DSSA-H can retrieve hurricane-relevant tweets with a trained supervised-learning classifier, Random Forest (RF), and classify the sentiment of hurricane-relevant tweets based on a domain-adversarial neural network (DANN). We built a dataset of tweets posted during six recent hurricanes and applied the DSSA-H approach for sentiment analysis. After evaluation, we found that each classifier (i.e., RF and DANN) outperforms baseline classifiers and that DSSA-H outperforms two high-performing general sentiment classification approaches when classifying sentiments of tweets posted during hurricanes. We also applied DSSA-H in examining sentiment patterns across six recent hurricanes in the U.S. This domain-specific sentiment analysis approach can be used by the first responders and affected communities to more accurately and rapidly detect crises and emergent events, allocate resources, and assess disaster's impact during hurricanes. DSSA-H contributes to an intelligent and adaptive disaster information system for the data-rich human and the built environment system.

中文翻译:

飓风期间推文的特定领域情感分析 (DSSA-H):一种基于领域对抗性神经网络的方法

摘要 飓风是美国最频繁、最具破坏性的灾害之一。这些事件规模大,影响相对长期。Twitter 等社交网络平台可以在大规模灾害(例如飓风)期间为灾害管理人员和受灾人群提供实时信息,但为灾害管理提取有用信息和准确解释数据仍然具有挑战性。社交媒体数据的情绪分析有助于检测受影响人群的担忧,并以前所未有的规模了解个人在实地的反应,但众所周知,该方法依赖于领域。相同的词或表达可以表示不同领域中的相反情绪。本文提出了一种专门针对飓风期间发布的推文 (DSSA-H) 的特定领域情感分析方法。DSSA-H 可以使用训练有素的监督学习分类器随机森林 (RF) 检索飓风相关推文,并基于域对抗性神经网络 (DANN) 对飓风相关推文的情绪进行分类。我们构建了一个在最近六次飓风期间发布的推文数据集,并应用 DSSA-H 方法进行情绪分析。经过评估,我们发现每个分类器(即 RF 和 DANN)都优于基线分类器,并且在对飓风期间发布的推文情绪进行分类时,DSSA-H 优于两种高性能的一般情绪分类方法。我们还应用 DSSA-H 来检查美国最近六场飓风的情绪模式 第一响应者和受影响社区可以使用这种特定于领域的情绪分析方法来更准确、更快速地检测危机和紧急事件、分配资源并评估飓风期间灾害的​​影响。DSSA-H 致力于为数据丰富的人类和建筑环境系统构建一个智能和自适应的灾害信息系统。
更新日期:2020-09-01
down
wechat
bug