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Text Analytics

Text Analytics is an installable Python library for introductory computational linguistics and NLP, offering text-feature extraction, classification, topic modeling, word2vec training, clustering, linguistic-distance searches, phrase analysis and corpus comparison.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Text Analytics — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use it for introductory NLP coursework or exploratory Python exercises when the task fits its documented feature, classification, topic-modeling, clustering or corpus-analysis methods.

Who it is for

  • Instructors preparing introductory computational linguistics or NLP exercises in Python.
  • Learners practicing text feature extraction, classification and unsupervised analysis.
  • Analysts exploring phrase patterns, clusters or similarities across text corpora.

Documented uses

  • Extract style, sentiment, content-word and syntactic-construction features from text.
  • Train shallow or MLP classifiers using text features and labels.
  • Explore topics, word vectors, document clusters and linguistically close documents.
  • Describe and compare corpora using word frequencies, phrase associations and similarity.
Before you choose

Requirements & limits

The public GitHub repository is licensed under GPL-3.0. The README documents installation with pip from the package name or directly from the repository.

  • The library is explicitly framed for introductory courses and examples.
  • The documented feature options include function-word n-grams, sentiment words, TF-IDF-weighted content words, PMI phrase finding and syntactic constructions.
  • The repository provides both shallow classification and MLP examples, plus several unsupervised and corpus-comparison methods.

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

Install and initialize text_analytics

Install the Python package and create a TextAnalytics instance.

Before you start

  • A Python environment with pip.

Steps

  1. Install the package with the command below, or install from the GitHub repository with the command below.

    Documented command
    pip install textanalytics
    Documented command
    pip install git+https://github.com/jonathandunn/text_analytics.git

  2. Import the class with from text_analytics import TextAnalytics.

  3. Create an instance with ai = TextAnalytics().

  4. Continue to the repository README for documented feature extraction, classification, and analysis examples.

Expected outcome

The package is installed and a TextAnalytics instance is initialized.

Guide limitations

  • The installation and initialization instructions do not specify Python-version requirements or verify that installation succeeded.
  • The documented analysis examples require inputs such as df, labels, or files whose preparation is not described here.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
jonathandunn/text_analytics · Official homepage title · retained in its source language
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