论文标题
Twitter数据分析：IZMIR地震案例
Twitter Data Analysis: Izmir Earthquake Case
论文作者
论文摘要
Türkiye位于断层线上；地震通常发生在大小规模上。需要有效的解决方案来收集灾难期间的当前信息。我们可以使用社交媒体来深入了解公众舆论。该见解可用于公共关系和灾难管理。在这项研究中，分析了2020年10月发生的有关IZMIR地震的Twitter帖子。我们质疑该分析是否可以按时进行社会推断。数据挖掘和自然语言处理（NLP）方法用于此分析。 NLP用于情感分析和主题建模。潜在的DIRICHLET分配（LDA）算法用于主题建模。我们使用了来自变形金刚（BERT）模型的双向编码器表示，用于变形金刚架构进行情感分析。结果表明，用户分享了他们的善意愿望，并旨在为地震后的发起援助活动做出贡献。用户希望使主管机构和组织的声音表达自己的声音。提出的方法有效地工作。还讨论了未来的研究。
Türkiye is located on a fault line; earthquakes often occur on a large and small scale. There is a need for effective solutions for gathering current information during disasters. We can use social media to get insight into public opinion. This insight can be used in public relations and disaster management. In this study, Twitter posts on Izmir Earthquake that took place on October 2020 are analyzed. We question if this analysis can be used to make social inferences on time. Data mining and natural language processing (NLP) methods are used for this analysis. NLP is used for sentiment analysis and topic modelling. The latent Dirichlet Allocation (LDA) algorithm is used for topic modelling. We used the Bidirectional Encoder Representations from Transformers (BERT) model working with Transformers architecture for sentiment analysis. It is shown that the users shared their goodwill wishes and aimed to contribute to the initiated aid activities after the earthquake. The users desired to make their voices heard by competent institutions and organizations. The proposed methods work effectively. Future studies are also discussed.
