  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.
