Natural Language Processing is a subfield of Artificial Intelligence that works on making the human language understandable to the machine/computer. NLP has different functionalities that work on the textual data and find out useful insights and information. NLP can practically be used for Speech Recognition, creating voice search engines, etc. NLP can be used to perform a large variety of operations on text data like tokenizing, lamenting, stemming POS tagging, etc.
NLP Profiler is a simple NLP library which works on profiling of textual datasets with one one more text columns. Basically NLP profilers provide us with high-level insights about the data along with the statistical properties of the data. It works the same way as pandas.describe() works for pandas dataframe for statistical properties.
It takes the textual data as input with at least one column with text data and returns a dataframe which contains useful insights about the data like sentiment analysis, the subjectivity of data, etc. NLP profiler is in its early stage and is continuously improving.
In this article, we will explore what are the different functionalities that are there in NLP profiler and implement them in order to gain useful insights from the data.
NLP Profiler can be installed using the git repository where it is hosted. Before Installing it you need to download and install the git version according to your operating system. After git is installed we can install NLP Profiler by running the below-given command in the command prompt.
We will load the data using pandas so we will import pandas and for creating the data profile we will import the NLP profiler.
import pandas as pd
from nlp_profiler.core import apply_text_profiling
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