Topic Modeling
Topic modeling is an essential process of NLP concept which actually is the process of extracting themes from a given corpus of text data and the requirement of topic modeling is because the amount of the written material we encounter in our day to day life is simply beyond our processing capacity. Topic models can help us to organize and also offer insights for us to understand large collections of unstructured data which is present or made available to us by any means.
Talking about the brief history of topic modeling, it was developed in 1998 by Raghvan Tamaki, and then came the BLSA which is known as the probabilistic latent semantic analysis which was created by Thomas Hoffman and the most commonly used technique called the LDA which is the latent desolate allocation which was developed in the year 2002 by Andrew ng, David Bly and Michael Jordan. Another alternative to LDA is HLTA which is known as the Hierarchical Latent Tree Analysis which actually models the occurrence of words using a tree of various latent variables and the states of the latent variables which corresponds to the soft clusters of documents which are interpreted as topics.
When we come to discuss about the statistical significance we can say that -:
· The group of words which has been occurring together.
· The second one is about the similar terms and inverse document frequencies interval.
· The other example is frequently occurring words together.
When we take a simple example of Wikipedia we find that there are billions of documents which are covering every aspect that is about almost all the topics a human can come across and is expanding as well daily. So a ideology to discover this automatically is what the goal is. This way can be really turn out to be very useful for people who are looking to explore the data or facts in the Wikipedia. By the following process new and emerging topics can be discovered easily and necessary stuffs can be written about them in order to expand information or even in case to imply correction or modification on a particular topic.
Let us now discuss about the steps involved in the Topic Modeling in details.
The first one is the Latent Dirichlet Allocation Topic Modeling. In the Natural Language Processing , LDA is referred to a generative statistical model which allows sets of sample observation to be explained by the unobserved groups that actually tell us that why data which is present before us has some similar parts in it. LDA is known as a three level hierarchical Bayesian Model where each item in set of collection is modeled as one finite set of topics present. Further each topic in turn is then modeled as an infinite mixture over an underlying set of presentable topics. In the following context of the text modeling, the probability provides us with an explicit representation of a particular set of documents. LDA is highly modular in nature and can be extended or modified easily, as it can be further effectively used to quantify a particular relationship between a specific topic which needs to be found. Topic modeling is also a type of of statistical model that is used for discovering abstract topics. Topic modeling is therefore also used as a text mining tool for the discovery of hidden detail structures in a text body. Other option is the nltk which can be used as an alternative to tokenize and tag for topic modeling. The next important feature is Vader that comes from nltk and is a vital tool for sentimental analysis. It generally takes capital and exclamation mark into account which adds value in sentimental analysis like online feedback form, twitter comments facebook comments etc.
