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Nlp Profiler

Nlp Profiler is an open-source Python library that creates high-level NLP insights and granular text statistics for one or more text columns in a dataset, returning a dataframe of per-row features.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Nlp Profiler — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it during dataset exploration or preprocessing when you want a structured view of a text column before deciding how to handle it in an NLP workflow.

Who it is for

  • Data practitioners exploring datasets with free-form text fields
  • Python users adding text-feature profiling to an NLP data preparation workflow

Documented uses

  • Generate per-row sentiment, objectivity, subjectivity, spelling, grammar and readability features.
  • Count text characteristics such as characters, words and emojis.
  • Summarize generated numerical features with pandas descriptive statistics.
Before you choose

Requirements & limits

  • The documented interface accepts a dataset and text-column name and returns a new dataframe.
  • Python 3.7 or later and the project's requirements.txt dependencies are listed prerequisites.
  • Grammar checks need internet access and Java 8 or later.
  • The README advises that high-level profiling may need more processing power, RAM and working disk space as dataset size grows.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Profile a text column in an existing dataset

Add per-row text features for one dataset column using the library's documented function.

Before you start

  • Python 3.7 or later
  • Dependencies listed in the repository's requirements.txt
  • A dataset and the name of a column containing text

Steps

  1. Install the library from PyPI with the command below.

    Documented command
    pip install -U nlp_profiler

  2. Import nlp_profiler.core as nlpprof.

  3. Call nlpprof.apply_text_profiling(dataset, 'text_column') and assign its returned dataframe.

Expected outcome

A new dataframe containing per-row features for the selected text column, which can be summarized with pandas descriptive statistics.

Guide limitations

  • The README notes that higher-level profiling, including grammar checks, can require more processing power, RAM and 1–3 GB of working disk space depending on dataset size.
  • Grammar checks require internet access and Java 8 or later.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
neomatrix369/nlp_profiler · Official homepage title · retained in its source language
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