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Is it possible to apply LDA separately on each sentence?
Also, Would applying LDA separately on each sentence of the document yield different results (i.e. topics extracted) from if I applied LDA to the whole document? Yes, it is possible to assign topics to sentences, or, more generally, to give each sentence a probability of belonging to each topic.
How is LDA used to classify text in a document?
I encourage you to pull it and try it. LDA is used to classify text in a document to a particular topic. It builds a topic per document model and words per topic model, modeled as Dirichlet distributions. Each document is modeled as a multinomial distribution of topics and each topic is modeled as a multinomial distribution of words.
How to choose the right topics for LDA?
LDA assumes that the every chunk of text we feed into it will contain words that are somehow related. Therefore choosing the right corpus of data is crucial. It also assumes documents are produced from a mixture of topics.
How does topic modeling work in LDA applications?
Topic modeling works in an exploratory manner, looking for the themes (or topics) that lie within a set of text data. There is no prior knowledge about the themes required in order for topic modeling to work. It discovers topics using a probabilistic framework to infer the themes within the data based on the words observed in the documents.
How is the topic prevalence determined in LDA?
The answer here is no. In deciding what topic each word of the corpus comes from, LDA inference algos borrow information from what other words are in the corpus through a parameter (denoted by θ in the original LDA paper) which gives the topic prevalence for each document.
Is it possible to assign a topic to a sentence?
Yes, it is possible to assign topics to sentences, or, more generally, to give each sentence a probability of belonging to each topic. Many LDA inference methods provide a probability of each word belonging to each topic, which you can simply aggregate by averaging to determine the probability of each sentence belonging to each topic.