Comparative analysis with topic modeling and word embedding methods after the Aegean Sea earthquake on Twitter

Topic detection from Twitter is a significant task that provides insight into real-time information. Recently, word embedding methods and topic modeling techniques have been utilized to find latent topics in various fields. Detecting topics leads to effective semantic structure and provides a better understanding of users. In the proposed study, different types of topic detection techniques are utilized, which are latent semantic analysis (LSA), Word2Vec, and latent Dirichlet allocation (LDA), and their performances are evaluated by the implementation of the K-means clustering technique on a real life application. In this case study, tweets were gathered after an earthquake with a magnitude of 6.6 on the Richter scale that took place on October 30, 2020, on the coast of the Aegean Sea (Izmir), Turkey. Tweets are clustered under fifteen hashtags separately, and the aforementioned techniques are applied to data-sets which vary in size. Therefore, the novelty of the proposed paper can be expressed as the comparison of different topic models and word embedding methods implemented for different sizes of documents in order to demonstrate the performance of these methods. While Word2Vec gives good results in small data-sets, LDA generally gives better results than Word2Vec and LSA in medium and large data-sets. Another aim of the proposed study is to provide information to decision makers for supporting victims and society. Therefore, the general situation of society is analyzed and society's attitude is demonstrated for decision-makers to take actionable activities such as psychological support, educational support, financial support, and political activities, etc.