Abstract
Today, with the increasing use of social media platforms, many sociological problems that did not exist before have emerged. The difficulty of analyzing the resulting large amounts of data using traditional methods makes it difficult to understand, investigate these problems and produce the necessary solutions. For this reason, natural language processing studies performed using artificial intelligence algorithms have become popular again in the literature. In this study, twittler/messages sent about the december on the agenda in a certain time interval via the Twitter application, which is a social media platform, were compiled. After evaluations such as bi-gram and tri-gram were performed on the data set created, Topic modeling analysis (Topic Modeling) was performed using Latent Dirichlet Analysis (LDA) method because the data was unlabeled. Finally, the results obtained have been evaluated and the situations that may arise with natural language processing and the problems that can be proposed for solution have been revealed.
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Koçak, Ç., Yiğit, T., Anitha, J., Mustafayeva, A. (2023). Topic Modeling Analysis of Tweets on the Twitter Hashtags with LDA and Creating a New Dataset. In: Smart Applications with Advanced Machine Learning and Human-Centred Problem Design. ICAIAME 2021. Engineering Cyber-Physical Systems and Critical Infrastructures, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-031-09753-9_41
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