A Systematic Review on Integration of Big Data with Natural Language Processing (NLP)

Author(s): Ankur N Shah

Abstract:

The vast amount of data which is managed across internet and the way of management can be changed by using combination of Big Data and Natural Language Processing (NLP). This paper mainly provide idea how we can combine big data technology with NLP techniques, systematic review for recent advancements in Big Data driven NLP system and also provide various tools and approaches that can be helpful for efficient large scale text processing. To find, evaluate, and contrast pertinent research contributions across distributed computing frameworks like Apache Spark, Hadoop, and real-time streaming platforms, an organized technique is used. The study offers a comparison of the structures, methods, and tools utilized in extensive NLP applications. The major tool which is Apache Spark NLP, aimed to fast-track NLP tasks over the multiple computers. This paper check the latest studies and find encroachments in fields along with handling large data set such as determining individuals' emotions from text, extracting key information, categorizing text into topics, and comprehending language Additionally, based on data processing layers, computational frameworks, and application domains, a novel taxonomy for Big Data NLP systems is suggested. Scalability, real-time processing, and model explain-ability are among the major issues that are rigorously examined along with current solutions. The results demonstrate how Big Data and NLP integration greatly improves processing efficiency and permits intelligent real-time applications in fields like social media analytics, healthcare, and finance. The conclusion part of the paper mainly highlights about additional research and possible further way required to use Big Data with NLP. This paper mainly benefits to those who wants to enhance their knowledge about how to combine two major technologies like Big Data and NLP.

keywords:
Big Data, Natural Language Processing, Spark NLP, Distributed Systems, Stream Processing, Scalable NLP, Text Analysis.

Pages: 92-97     |    12 View     |    1 Download