Latent Dirichlet Allocation, also known as LDA, is one of the most popular methods for topic modelling. Using LDA, we can easily discover the topics that a document is made of. LDA assumes that the documents are a mixture of topics and each topic contain a set of words with certain probabilities. For example, consider the below sentences: Apple and Banana are fruits. I bought a bicycle recently. In less than two years, I will buy a bike. The colour of the apple and bicycle are red. The output of LDA would look like this: Topic 1 : 0.7*apple + 0.3*banana Topic 2 : 0.6*bicycle + 0.4*bike Sentence 1 : [(Topic 1, 1), (Topic 2, 0)] Sentence 2 : [(Topic 1, 0), (Topic 2, 1)] Sentence 3 : [(Topic 1, 0.5), (Topic 2, 0.5)] Please note that the above probabilities are made up numbers for intuition. To extract the topics and probability of words using LDA, we should decide the number of topics (k) beforehand. Based on that, LDA discovers the topic distribution of docum
Data Scientist | Tech Blogger