Modelling techniques for unstructured data using machine learning and artificial intelligence approach

Abstract

Intially, the concept of document models is discussed with respect to the Bernoulli approach, that is, basis is the presence or absence of primary blocks of the documents, namely tokens. The problem primarily deals with how an unstructured dataset consisting of text documents is converted to structured content with mathematical and statistical foundation. newlineThe focus in another problem is on Multinomial document model. It is similar to the Bernoulli model, but the presence flag in the former is now replaced with the frequentist method which takes into account the number of times the tokens occur in the text. newlineIn next problem, we move onto research of unsupervised topic modeling techniques to obtain latent topical structure across text documents and further fine-tuning with help of machine learning. Problem 4 deals with obtaining of key conversational drivers for textual data coupled with the attached sentiment and mood states. Such an approach helps in detecting the key drivers of conversation that is, whether it is having a positive impact or not or whether the text content has potential to become viral and much more. newlineIn last problem, we focus on Mood State and Behavior Prediction model in Social Media through Unstructured Data Analysis. Behavior Dirichlet Probability Model (BDPM), which can capture the Behavior and Mood of user on Social Media.