So, basically, you are evaluating on the qualitative approach, as there is no quantitative measure involved, which can tell you how much worse your dispatch product quality at A is compared to dispatch quality at B. You may need to improve your process if most people give you bad reviews. One way is to collect the reviews from various people – for example- “whether they receive product in good condition”, Did they receive on time”. Imagine you are a lead quality analyst sitting at location X at a logistics company and you want to check the quality of your dispatch product at 4 different locations: A, B, C, D. Let’s start learning with a simple example and then we move to a technical part of topic coherence. This is an attractive method to bring structure to otherwise unstructured text data, but Topics are not guaranteed to be well interpretable, therefore, coherence measures have been proposed to distinguish between good and bad topics. Topic models learn topics-typically represented as sets of important words-automatically from unlabelled documents in an unsupervised way. Latent Dirichlet Allocation (LDA) is a widely used topic modeling technique to extract topic from the textual data. There are many techniques that are used to obtain topic models. ![]() Topic modeling provides us with methods to organize, understand and summarize large collections of textual information. In this article, we will go through the evaluation of Topic Modelling by introducing the concept of Topic coherence, as topic models give no guaranty on the interpretability of their output.
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