AN IMPROVED PATTERN BASED LDA TOPIC MODELING FOR BUSINESS INTELLIGENCE

Abstract

Topic models give a helpful strategy to dimensionality decrease and exploratory data analysis in huge text corpora. Most ways to deal with topic model learning have been founded on a greatest likelihood objective. Proficient algorithms exist that endeavor to inexact this target, yet they have no provable certifications. As of late, algorithms have been presented that give provable limits, however these algorithms are not down to earth since they are wasteful and not hearty to infringement of model presumptions. In this work, we propose to consolidate the statistical topic modeling with pattern mining strategies to produce pattern-based topic models to upgrade the semantic portrayals of the conventional word based topic models. Using the proposed pattern-based topic model, clients' inclinations can be modeled with different topics and every one of which is addressed with semantically rich patterns. A tale information filtering model is proposed here. In information filtering model client information needs are made in terms of different topics where every topic is addressed by patterns. The calculation produces results similar to the best executions while running significant degrees quicker.