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首页> 外文期刊>Computers，environment and urban systems >Domain-specific sentiment analysis for tweets during hurricanes (DSSA-H): A domain-adversarial neural-network-based approach
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Domain-specific sentiment analysis for tweets during hurricanes (DSSA-H): A domain-adversarial neural-network-based approach

机译：飓风期间推特的域特异性情绪分析（DSSA-H）：基于领域的侵犯神经网络的方法

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摘要

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.
机译：飓风是美国最常见和最具破坏性的灾害之一。事件大规模，具有相对长期的影响。诸如Twitter之类的社交网络平台可以为灾难管理者提供实时信息，并在大规模灾害（例如，飓风）中为受影响的人群提供实时信息，但为灾害管理准确提取有用的信息和解释数据仍然具有挑战性。社交媒体数据的情感分析有助于检测受影响人民的担忧，并在前所未有的尺度上了解个人对地面的反应，但已知该方法依赖于域名。相同的单词或表达可以指示不同域中的相反情绪。本文提出了专门针对飓风（DSSA-H）发布的推文的特异性情绪分析方法。 DSSA-H可以通过训练有素的监督学习分类器，随机森林（RF）以及基于领域 - 对冲神经网络（DANN）对飓风相关推文的情绪进行检索相关的推文。我们在最近的六个飓风期间建立了一个推文的数据集，并应用了DSSA-H方法进行情感分析。在评估之后，我们发现每个分类器（即，RF和DANN）优于基线分类器，并且DSSA-H在分类飓风期间发布的推文的情绪时，DSSA-H优于两种高度常规情绪分类方法。我们还应用DSSA-H在美国六个飓风中的六个飓风中审查了这种域特定情绪分析方法，可以由第一响应者和受影响的社区使用，更准确，快速地检测危机和紧急事件，分配资源和评估灾难在飓风中的影响。 DSSA-H为数据丰富的人类和内置环境系统做出了智能和自适应灾害信息系统。

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