T\"urkiye 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.

翻译：T\\"urkiye"位于断层线上;地震经常大规模和小规模地发生。在灾害期间收集当前信息需要有效的解决办法。我们可以使用社交媒体来深入了解公众舆论。这种洞察力可以用于公共关系和灾害管理。在这项研究中,对2020年10月伊兹米尔地震的推特文章进行了分析。我们质疑这一分析是否可用于在时间上进行社会推理。这项分析使用了数据挖掘和自然语言处理(NLP)方法。NLP用于情绪分析和主题建模。潜在dirichlet分配(LDA)算法用于主题建模。我们使用来自变换器的双向电解码器模型进行情绪分析。我们使用与变换器结构合作的双向解码器模型进行情绪分析。我们发现,用户分享了他们的善意愿望,目的是为地震后启动的援助活动作出贡献。用户希望让主管机构和组织听到他们的声音。拟议的方法也有效发挥作用。