Python NLP Text Analysis

This project aims to analyze text data from 10-K forms, focusing on the core values such as "innovation", "integrity", "quality", "respect", and "teamwork". The program scans through a database of documents and identifies the frequency of these core values in different forms of n-grams (unigram, bigram, trigram). The results are then stored in a SQLite database.

Requirements

The code requires the following Python libraries:

• concurrent.futures - for parallel processing.
• spacy - for NLP operations like tokenization, lemmatization, etc.
• pandas - for data handling.
• sqlalchemy - for handling the SQLite database.
• textacy - for creating ngrams.
• glob - for file handling.

Also, you need to have the en_core_web_sm Spacy model, which can be installed via:

python -m spacy download en_core_web_sm

The SQLite database database.sqlite should be present at the root directory. The text data is expected to be found under a directory structure ./data/YEAR/QUARTER/FILE.txt.

Installation

Clone the repository:

git clone https://github.com/mmcodesso/textmining-parsing10k-wordcount.git
cd textmining-parsing10k-wordcount

Create and activate a virtual environment:

python3 -m venv env
source env/bin/activate  # Unix/MacOS
.\env\Scripts\activate   # Windows

Install the requirements:

pip install -r requirements.txt

Download the required Spacy model:

python -m spacy download en_core_web_sm

Usage

Run the script via the command line:

python main.py

The script generates a list of 10-K forms from the data directory, excluding previously processed files. Then, it uses multi-processing to process these files concurrently, cleaning the text, creating ngrams, and counting the occurrences of core values.

Contribution

Contributions, issues and feature requests are welcome. Feel free to check issues page if you want to contribute.

License

This project is licensed under the terms of the MIT license.

# MIT License

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