  With the development of technology, the use of social media has become quite
common. Analyzing comments on social media in areas such as media and
advertising plays an important role today. For this reason, new and traditional
natural language processing methods are used to detect the emotion of these
shares. In this paper, the Latent Dirichlet Allocation, namely LDA, and
Non-Negative Matrix Factorization methods in topic modeling were used to
determine which emotion the Turkish tweets posted via Twitter. In addition, the
accuracy of a proposed n-level method based on LDA was analyzed. Dataset
consists of 5 emotions, namely angry, fear, happy, sad and confused. NMF was
the most successful method among all topic modeling methods in this study.
Then, the F1-measure of Random Forest, Naive Bayes and Support Vector Machine
methods was analyzed by obtaining a file suitable for Weka by using the word
weights and class labels of the topics. Among the Weka results, the most
successful method was n-stage LDA, and the most successful algorithm was Random
Forest.
