Özet:

In this study, firstly, tweets containing specific words on the Twitter platform were obtained and pre-processed using the RapidMiner software. After that, the tweets are clustered based on the topic modeling approach. “Search Twitter”, “Select Attributes”, and “Nominal to Text” blocks were used for preprocessing. This preprocessed data is then analyzed using “Tokenize”, “Aggregate”, and “Discretize” operators. The most used words were determined, and tweets are grouped according to their frequencies. Then, it is explained how to perform topic-based modeling and clustering on Twitter data. “Extract Topics From Documents (LDA)” operator, which uses the Latent Dirichlet Allocation model, was used for this process. The most commonly used words in tweets, and the number of tweets per user were extracted and investigated via tables and graphical illustrations. In addition, the word cloud of each topic, obtained as a result of the topic modeling process, was created.

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