A simple NLP library allows profiling datasets with one or more text columns. When given a dataset and a column name containing text data, NLP Profiler will return either high-level insights or low-level/granular statistical information about the text in that column.
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A simple NLP library allows profiling datasets with one or more text columns.
NLP Profiler returns either high-level insights or low-level/granular statistical information about the text when given a dataset and a column name containing text data, in that column.
In short: Think of it as using the
pandas.describe() function or running Pandas Profiling on your data frame, but for datasets containing text columns rather than the usual columnar datasets.
Table of contents
- What do you get from the library?
- Getting started
- Credits and supporters
What do you get from the library?
- Input a Pandas dataframe series as input paramater.
- You get back a new dataframe with various features about the parsed text per row.
- high-level: sentiment analysis, objectivity/subjectivity analysis, spelling quality check, grammar quality check, etc…
- low-level/granular: number of characters in the sentence, number of words, number of emojis, number of words, etc…
- From the above numerical data in the resulting dataframe descriptive statistics can be drawn using the
pandas.describe()on the dataframe.
Under the hood it does make use of a number of libraries that are popular in the AI and ML communities, but we can extend it’s functionality by replacing or adding other libraries as well.
A simple notebook have been provided to illustrate the usage of the library.
Please join the Gitter.im community and say “hello” to us, share your feedback, have a fun time with us.
Note: this is a new endeavour and it’s may have rough edges i.e. probably NOT capable of doing many things atm. Many of these gaps are opportunities we can work on and plug, as we go along using it. Please provide constructive feedback to help with the improvement of this library. We just recently achieved this with scaling with larger datasets.
- Python 3.6.x or higher.
- Dependencies described in the
- High-level including Grammar checks:
- faster processor
- higher RAM capacity
- working disk-space of 1 to 3 GBytes (depending on the dataset size)
- Jupyter Lab (on your local machine).
- Google Colab account.
- Kaggle account.
- Grammar check functionality:
- Internet access
- Java 8 or higher
Look at a short demo of the NLP Profiler library at one of these:
pip install nlp_profiler
From the GitHub repo:
pip install git+https://github.com/neomatrix369/nlp_profiler.git@master
From the source (only for development purposes), see Developer guide
import nlp_profiler.core as nlpprof new_text_column_dataset = nlpprof.apply_text_profiling(dataset, 'text_column')
from nlp_profiler.core import apply_text_profiling new_text_column_dataset = apply_text_profiling(dataset, 'text_column')
See Notebooks section for further illustrations.
See Developer guide to know how to build, test, and contribute to the library.
After successful installation of the library, RESTART Jupyter kernels or Google Colab runtimes for the changes to take effect.
See Notebooks for usage and further details.
Credits and supporters
Refer licensing (and warranty) policy.
Contributions are Welcome!
Please have a look at the CONTRIBUTING guidelines.
Please share it with the wider community (and get credited for it)!
Go to the NLP page