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DMISummerSchool2024

Repository with code for NLP preprocessing

Overview

This NLP pipeline is designed to process and analyze textual data, focusing on tasks such as language detection, text cleaning, Named Entity Recognition (NER), and dependency relation extraction. The pipeline is built using R and various R packages.

Description

This code implements a Natural Language Processing (NLP) pipeline for processing textual data. It includes steps for data loading, cleaning, and analysis using various R libraries. The code is designed to handle text data, such as tweets, and perform tasks such as language detection, emoji removal, URL extraction, hashtag extraction, and dependency parsing. The processed data is then used to build unigram and association matrices for further analysis.

Key Features

Data Loading:

Reads data from a CSV file using the readr library.

Language Detection:

Uses the textcat package to infer the language of the text.

Data Cleaning:

Removes emojis, URLs, hashtags, and special characters. Extracts relevant features like hashtags, finance tags, and standard character emoticons.

NLP Processing:

Utilizes the spacyr package for Named Entity Recognition (NER) and dependency parsing. Custom function mySpacyrConsolidateEnt consolidates entity recognition results.

Data Preparation for Analysis:

Generates unigram and association Document-Feature Matrices (DFMs) using the quanteda package. Groups and filters DFMs, removing stopwords and other irrelevant features.

Visualization:

Creates word clouds for unigrams and associations. Builds dependency relation networks for visualization with visNetwork.

Initial Creation Date

03/11/2021

Current Version

1.54

Current Version Date

11/07/2024

Author

Carlo Santagiustina

Contact

[email protected]

Confidentiality

See licence.

Requirements

R Libraries

The following R libraries are required to run this pipeline:

  • devtools
  • tidyverse
  • quanteda
  • ggplot2
  • stm
  • spacyr
  • visNetwork To install the necessary packages, run:
install.packages(c("devtools","tidyverse","quanteda","ggplot2","stm","spacyr","visNetwork"))
devtools::install_github("hadley/emo")

System Dependencies: Python and Spacy

The spacyr package requires a Python virtual environment with the spacy library and specific language models installed. The pipeline uses Miniconda to manage the Python environment.

reticulate::install_miniconda(path = "~/Documents/Miniconda", update = TRUE, force = FALSE)
Sys.setenv(RETICULATE_MINICONDA_PATH="~/Documents/Miniconda")
reticulate::condaenv_exists()

Create a Conda environment for spacy:

reticulate::conda_create(envname = "spacyr", packages = "spacy", forge = TRUE, channel = "conda-forge")

Download the necessary language model (e.g., French):

spacyr::spacy_download_langmodel(envname = "spacyr", model = "fr_core_news_md")

Initialize spacyr with the language model:

spacy_initialize('fr_core_news_md', condaenv = "~/Documents/Miniconda/envs/spacyr/")