This dataset is designed for teaching a topic modeling technique called Latent Dirichlet Allocation (LDA), which is used to find latent topic structures in text data. The dataset is a subset of data derived from the 2016 News Articles dataset, and the example investigates the topics discussed in the news articles in an automated fashion. The dataset file is accompanied by a. The Latent Dirichlet Allocation (LDA), a topic modeling algorithm, was used to determine which emotions the tweets on Twitter had in the study. The dataset consists of 4000 tweets that are categorized into 5 different emotions that are anger, fear, happiness, sadness, and surprise. Zemberek, Snowball, and first 5 letters root extraction methods are used to create models. The.