Topic modeling is a text analytics technique that is used to identify and extract the major topics from a text document. It can be used to automatically summarize a large document or to cluster documents by topic. Topic Modeling is similar to other techniques such as Latent Dirichlet Allocation (LDA) and latent Semantic Analysis (LSA). However, Topic Modeling is unique in that it does not require a large amount of training data. It can be used on any text document, regardless of length or subject matter.
Disadvantages of Topic Modeling
The main disadvantage of Topic Modeling is that it is not a perfect tool. It may extract topics that are not relevant to the document, or it may not extract all of the topics from a document. However, it is still a valuable tool for text analytics because it can help you to quickly and easily identify the most important topics in a document.
Tools Used to Perform Topic Modeling
Several different software packages can be used to perform Topic Modeling. Some of the most popular ones include:
- Topic Modeler
- Latent Dirichlet Allocation
These software packages all have their advantages and disadvantages. You will need to experiment with each one to find the one that works best for your needs.