https://github.com/oroszgy/hungarian-text-mining-workshop

Materials for the Text Mining workshop held in the HuNLP meetup, June 2017
https://github.com/oroszgy/hungarian-text-mining-workshop

classification hungarian information-extraction keyword-extraction machine-learning meetup natural-language-processing nlp python scikit-learn sentiment-analysis spacy spacy-models text-mining text-mining-workshop textacy tutorial workshop

Last synced: 2 months ago
JSON representation

Materials for the Text Mining workshop held in the HuNLP meetup, June 2017

• Host: GitHub
• URL: https://github.com/oroszgy/hungarian-text-mining-workshop
• Owner: oroszgy
• License: mit
• Created: 2017-06-22T20:11:07.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2022-04-06T18:45:01.000Z (about 2 years ago)
• Last Synced: 2024-01-26T20:32:05.793Z (5 months ago)
• Topics: classification, hungarian, information-extraction, keyword-extraction, machine-learning, meetup, natural-language-processing, nlp, python, scikit-learn, sentiment-analysis, spacy, spacy-models, text-mining, text-mining-workshop, textacy, tutorial, workshop
• Language: Jupyter Notebook
• Homepage:
• Size: 18.6 MB
• Stars: 20
• Watchers: 3
• Forks: 6
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Lists

• awesome-hungarian-nlp - Tutorial on Text Mining for Hungarian

README

        
# Text mining workshop

## Preparation for the workshop

Please be prepared with

* basic knowledge of Python

* experience in using Jupyter notebooks

During the course we will use little bit of Pandas ([10 minute intro](https://pandas.pydata.org/pandas-docs/stable/10min.html)) and [scikit-learn](http://scikit-learn.org/stable/) to build simple machine learning models.

## Install dependencies and run the notebooks

### The easy way: using Docker

Get the docker image: `docker pull oroszgy/hungarian-text-mining-workshop`

Start Jupyter Notebook: `make start`

### The hard way: installing the packages manually

0. Make sure you have Python 3.5+ installed (preferably a conda distribution)

1. Clone this repository: `git clone http://github.com/oroszgy/hungarian-text-mining-workshop && cd hungarian-text-mining-workshop`

2. Install the necessary packages: `pip install -r requirements.txt`

3. Download the Enlgish and the Hungaruan NLP models for spaCy:

    * `python -m spacy download en`

    * `pip install https://github.com/oroszgy/spacy-hungarian-models/releases/download/hu_tagger_web_md-0.1.0/hu_tagger_web_md-0.1.0.tar.gz`

4. Install HuNlpy

    * `pip install https://github.com/oroszgy/hunlp/releases/download/0.2/hunlp-0.2.0.tar.gz`

Start Jupyter Notebook: `jupyter notebook`

## Table of Contents

1. [Practical NLP in Python: `spaCy` and `textacy`, Describing documents with words](./1_Intro.ipynb)

2. [Document categorization, Sentiment analysis](./2_TextCategorization.ipynb)

3. [Extracting named entities and concepts](./3_EntitiesAndConcepts.ipynb)

## Softwares used

* [spaCy](https://spacy.io)

* [Hungarian model for spaCy](https://github.com/oroszgy/spacy-hungarian-models)

* [textacy](http://textacy.readthedocs.io/)

* [scikit-learn](http://scikit-learn.org/stable/)

* [HuNlp](https://github.com/oroszgy/hunlp)

* [DBpedia Spotlight](http://www.dbpedia-spotlight.org/)

---

(c) Gyorgy Orosz, 2017