Introduction:
The paper entails a detailed discussion and analysis of “Natural Language Processing” within Big Data analysis. Big Data Analysis is basically referred to as the use of various advanced “analytical techniques” against very large and diverse sets of data and which tends to include “semi-structured”, ‘structured”, and “unstructured data” (Ghavami, 2019). These data have a wide range of sources that vary in size tremendously starting from terabytes to zettabytes. It is imperative to note that this kind of data requires “Natural Language Processing (NLP)” which is a type of “Machine Learning Algorithm” which is used for analyzing content. Natural Language Processing is a hot area of discussion and this enables harnessing big data for deriving information with the help of various creative and innovative methodologies to generate important insights on various current and future market trends (Ghavami, 2019). The paper shed light on the various utilities of Natural Language Processing along with a real-life example of its use, goals, and results.
Overview of Natural Language Processing and its purpose:
Natural Language Processing is referred to as the process of automatic manipulation of “natural language” such as texts, speeches, and so on by means of software. In the modern days, Natural Learning Processing is not only confined to linguistic fields but has moved to many expanded areas with the extensive rise of computers (Rajput, 2020). The main purpose of NLP is to study the various patterns that tend to emerge within text entries in the big data by analyzing the “semantics” and the “linguistics” with the help of machine learning and statistics and also tends to extract the prominent entries and the associations within the of the context of the message of information provided by the consumers (Wang, Wang, and Alexander, 2015). The main mechanism of the working of NLP is to emphasize ‘the sentences for understanding the intent rather than focusing on only the words or a string of words. Some of the most commonly used methods employed in NLP ate “disambiguation”, “tagging parts of speech”, “auto summarization”, “extraction of relations”, “extraction of entities”, and understanding or recognizing the natural language (Rajput, 2020). Therefore, it can be said that NLP is basically a type of Artificial Intelligence (AI) that helps machines to read texts by simulating the ability of human beings in order to understand the language.

Utilization of the Natural Language Processing within Big Data Analysis:
Irrespective of any sector, all the businesses of modern data tend to depend on huge volumes of textual information. For instance, large-scale “law firms” work with huge amounts of research, past as well as ongoing “legal transaction documents”, emails, notes, and other big volumes of special and government-generated reference info (Poudyal et al., 2019). Apart from that, pharmaceutical companies would have huge amounts of clinical trial data and information, data, notes from the physicians, patients’ credentials, and so on (Wang, Wang, and Alexander, 2015). Due to the fact that these types of data or information are hugely composed of language, NLP for big data poses a potential opportunity for taking advantage of what consists in the large and expanding stores of language or content in order to uncover the patterns, trends and connections throughout the “disparate data sources” (Poudyal et al., 2019).
It is imperative to note that there are potential benefits of Natural Language Processing within Big Data (Poudyal et al., 2019). It enables to analyze of the rising amount of “unstructured data” such as texts, emails, SMSs, and voice notes in an accurate manner, and this in return yields appropriate insights into “human behavior” when these are all integrated with the “structured data” (Wang, Wang, and Alexander, 2015). Thus, it can be said that NLP has huge benefits use within the context of Big data. With the rising prevalence of NLP, human beings will be able to interact with machines in an unpredictable way and the machine-human collaboration would hence allow great improvements in a broad array of “human endeavors” such as “philanthropy”, “health”, “communication”, etc.
Example of NLP use and its goals and results:
One of the most relevant and interesting examples of real-life use of Natural Language Learning processing in the context of human-machine interaction is the use of Siri in iOS. This is an outstanding use of NLP in the field of interaction as it employs an auto-translation mechanism.
Apple’s Siri uses a range of advanced machine-learning technologies that are able to understand the commands of humans and tends to give a response (Hoy, 2018). The ability of Siri to detect the phrase “Hey Siri!” is basically because of a “recurrent neural network”, as well as a multi-staged curriculum learning (Hoy, 2018).
In the stage of “user enrolment”, the user utters a few phrases that create a statistical model for the voice of the user and in the recognition stage, the “speech input” is compared by the computer to the “user trained model” and hence the hence it determines whether or not it should be accepted. In the next step, the “speech vector” tends to get turned to the place and focuses on the features of the voice instead of the various surrounding factors (Hoy, 2018). This enables the word” hey Siri!” to get recognized by the machine in a different environment.
The main goal of this application of “NLP” is to enable the users of Apple devices to fetch answers to their questions” and to carry out various searches, finish actions, send messages many more like this (Kepuska and Bohouta, 2018). In order to meet the goals in a more effective manner, a “network structure” consisting of “256 layers of neurons” along with “sigmoid activations” and a linear layer of “100 neurons” is said to have the best outcomes (Kepuska and Bohouta, 2018).
Conclusion:
The paper has discussed the various aspects and emergence of Natural Learning Processes within Big Data along with an overview of the methodology of the process and its utilization within big data. The paper has also given an effective real-life example of Apple’s Siri which is one of the most fascinating instances of NLP application in the modern day.