Introduction
Nowadays data gathering and analysis of data are essential in every field of business for understanding the needs and passions of the user so that the organizations and companies can satisfy their customer’s essentials and expectations. For data analytics different fields of computer Science, technologies, statistics, algorithms, analytical tools are used according to the types of data to be processed and depending on the field that data belongs. Natural Language Processing is one of the data analytical fields of computer science which comprises linguistics and artificial intelligence which mainly concentrates the communications between computers and humans (Vismaya & Reynald, 2017). It processes and analyze huge natural language data which inference the computers to understand the documents and gives in formations about that documents. NLP supports computers to interact with humans in their own language and process other language related methods to help computers to read text data, hear voice data and interpret it. NLP emprise utilization of algorithms to recognize and draw out the rules of natural language which is in the form of unstructured data format in turn processed and changed into structured data in which the computer can easily understand.
NLP process functions based on rules which can take more time and effort of people. Some other performs with plenty of data using Statistical methods and use machine learning algorithms to obtain the inferences according to the data. The set of data is trained by the machine learning algorithms. With the help of the trained data model the data can be tested to predict the outcome or the result(Thanaki 2017)
NLP plays an important role in research. The research includes speech recognition, text classification, machine translation, question answering. The researches work on Natural Language Processing (NLP) collect the data related to the behavior of human being to process the language by understanding and hence to use the suitable tools and techniques to put together the computers to recognize and process the natural language(Vismaya, Reynald,2017). They converts the linguistic knowledge of data into a rule based implementation by means of Machine learning and Deep learning algorithms for simple manipulation and distribution of data in language processing.
In certain cases the machine learning program in python will be implemented by three step processing to look with the key words relating the event. The first step the NLP cleans the data as the initial stage. In the second step another form of cleaning such as vectorization or tokenization will be performed where the text is converted into tokens and in the third step the data is split to train and test for further processing.(Szlosek, Ferrett, 2016)
This chapter delivers the fundamental concepts of Language processing in Python such as text and word operations. It also gives the details about the preference of Python language for Language Processing and its advantages. It specifies the basic concept of variables, list, operators, looping statements in Python and explains how it can be implemented in language processing. It outlines the knowledge attainment of basic applications of NLP in Python, preparing the data set for NLP applications, Context free grammar, Stepping into NLTK, Raw text processing . It also specify how a structured program can be written using python, Categorizing and tagging of words, how an information can be extracted from a text, Syntactic and Semantic analysis and NLP applications. It also concentrates some of the research applications where NLP is applied and the challenges of NLP processing in the real time area of applications.
