Analyse Large Text Data Through Topic Modeling
Topic modeling is an unsupervised machine learning method that can scan the documents sets and identify the phrase patterns and words inside them, and spontaneously collection of the word groups and related to the expressions that right characterized the documents sets.
It gives us techniques to schedule, understand and review the huge textual information collections.
It is the part of natural language processing that is used to instruct the machine learning models. Topic modeling is the process of logically choosing words that represent a specific subject from inside the document.
From a business point of view, topic modeling delivers the best time- and effort-saving advantages.
Topic Modeling Techniques:
Topic modeling is all about logically correlating various words. Here are the three topic modeling techniques as follows;
1) (LSA) - Latent Semantic Analysis
Latent Semantic Analysis determines to context leverage across the words to get the hidden topics or concepts. In this technique, machines utilities the (TF-IDF) term frequency-inverse document frequency of identifying documents.
TF-IDF is the numerical statistics that resemble how necessary a word is to document inside the corpus.
2) pLSA- Probabilistic Latent Semantic Analysis
Probabilistic Latent Semantic Analysis (pLSA) was represented to resolve the representation challenge in LSA by substituting the SVD with the probabilistic model. pLSA refers to every entry in the TF-IDF matrix with the help of the probability.
In the equation of, P (D, W) = P(D) ∑ P(Z|D) P(W|Z) gives the joint probability recommends how similarly it is to identify a particular word inside a document depending on the topic distribution in it.
Whereas the other parameterization P (D, W) = ∑P(Z)P(D|Z) P(W|Z) indicates the probability which the document includes a provided topic, and here the word inside the document refers to the provided topic. The parameterization exactly indicates the LSA technique of the topic modeling.
3) (LDA) - Latent Dirichlet Allocation
Latent Dirichlet Allocation is the pLSA Bayesian version. The main concept is substituted with the Dirichlet allocations and the distribution comes along probability simplex sample. A probability simplex denotes the number sets which include that one. Suppose the set includes three numbers, it is well-known as the three-dimensional Dirichlet distribution.
The topic's entire desired number is fixed as ‘k's in the dimensional Dirichlet distribution. The LDA model verifies all the document, make every word to the k topics, and gives the word representation and documents for the provided topic.
Topic Modeling Algorithm
In the topic modeling algorithm, we have the algorithm for Latent Dirichlet Allocation. It runs with simple steps. The preprocessing we have to perform every text processing activity. By taking out the stopwords from every document.
- Assign n topic number which will be recognized with the LDA algorithm. How can identify the number of the right topic? of course, it is not a simple thing, and it is generally a trial and error method. We use several n values until we are happy with the outcome.
- Schedule all the words in each document to not permanent topic. It will upgrade in the next step randomly.
- In this step, we will follow via all documents. Each word of the document will be evaluated to two values.
- This document refers to the probability for the specific topic. It depends on how many words from this document denote the present topic word.
- The document proportion is scheduled to the latest topic word due to the present word.
Most of the time we work with the third step before starting the algorithm. Finally, we will check every document, identify the document. It is an important task depending on the words. In the end, we allotted the document for the topic.