May 06, 2024
Pre-processing of a textual corpus for a lexicographic analysis with Iramuteq
- Wendeline Swart1,2,
- Guillaume Cabanac1,2,3,
- Cécile Crespy4,2,3
- 1Université Toulouse III – Paul Sabatier (IRIT);
- 2Université de Toulouse;
- 3Institut Universitaire de France;
- 4Sciences Po Toulouse (LaSSP)
Protocol Citation: Wendeline Swart, Guillaume Cabanac, Cécile Crespy 2024. Pre-processing of a textual corpus for a lexicographic analysis with Iramuteq. protocols.io https://dx.doi.org/10.17504/protocols.io.14egn3n1ml5d/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: November 13, 2023
Last Modified: May 06, 2024
Protocol Integer ID: 90851
Keywords: lexicographic analysis, OECD reports, lexicographic analysis with iramuteq, textual corpus, lexicographic analysis software, corpus, topics of the corpus, metadata of each document, document, metadata, oecd report, pdf format, iramuteq, analysis
Abstract
This protocol presents a lexicographic analysis of a textual corpus composed of documents. In our case, we applied that method to a corpus of 41 OECD reports initially in PDF format. The metadata of each document (e.g., year, country) are considered as variables for the analysis. Among the lexicographic analysis software that are available, we picked Iramuteq, developed by LERASS, Université de Toulouse. The lexicographic analysis uncovers the topics of the corpus and their evolution. This protocol presents how to pre-process the corpus to avoid some pitfalls.
Image Attribution
Analysis of the corpus with Iramuteq.
Guidelines
Warning: Iramuteq relies on a random variable when processing the text. For the same corpus the resulting analyses (e.g., classes) will not appear in the same order. This is because the program processes the text segments in a random order. The results produced by Iramuteq based on the same corpus are highly similar despite this random component.
Caveat: keep a version of the files produced at all steps to backtrack if necessary. This proved useful when re-coding the corpus.
Materials
- Operating system: Windows Subsystem for Linux
- Corpus: Scanned and OCRized science policy country reports published by the OECD between 1962 and 1996. The corpus was limited to the concluding parts of the 41 reports considered.
Troubleshooting
Overall view of the protocol. This protocol takes as input multiple documents in PDF and produces, as output, a single file containing the text to be analysed with the Iramuteq software.
Corpus delineation. Open each PDF with Acrobat Reader Professional and extract the concluding parts (stored in discussion.pdf).
First step of the protocol.
Reformatting the input files.
Second step of the protocol.
Convert input files. Open each PDF with Acrobat Reader Pro and convert into the Microsoft Word format (File, Save as… DOCX format).
Correct OCR errors. For each DOCX file, open it, spot the OCR errors (most of them are underlined in red) and correct them. Delete the passages that are not useful for a lexicometric analysis: headers, footers, and tables without relevant textual contents.
Restore the paragraph structure. Insert a newline between paragraphs to delineate sentences that must be considered together. Remove irrelevant newlines between sentences: some reflect page
breaks in the PDF.
Manual pre-processing of the DOCX documents.
Third step of the protocol.
Identify compound terms. Compound terms are multi-word phrases that shouldn’t be splitted during the analysis, such as “science policy.” Underline all of them with the highlighting tool of Microsoft Word.
Uniformise common terms. identify the variations used for common terms in the corpus and recode variations to keep only one form. For instance, recode “Second World War”, “2nd World War”, “WW2” into “World_War_2” (note the underline character ‘_’ instead of spaces). Another example: recode “European Union”, “EU”, “EEC”, “Common Market” into “European_Union”.
Delete irrelevant terms. Some terms convey no meaning for the lexicometric analysis. Remove them to prevent Iramuteq from inferring similarities between paragraphs only because these terms appear in several paragraphs. In the OECD reports, the name of the examiners are irrelevant (e.g., “Prof. X”).
Remove ‘ego’ in a document. For a report on Country “X”, remove all occurrences of “X” (i.e., ego) in the text. This prevents Iramuteq from making connections between paragraphs that do not use the same ideas but contain occurrences of the word “X” only.
Fourth step of the protocol.
Merge all documents. Merge the DOCX files into a single DOCX, prefixing each text with the necessary Iramuteq metadata, inferred from the file names. The top line added reads as:
**** *variable1_value1 *variable2_value2 … *variableN_valueN
For instance, the 1966 report from France is prefixed by:
**** *COUNTRY_FRANCE *YEAR_1966
Re-coding of all variants of established phrases to prevent them from being split.
Fifth step of the protocol.
Extract underlined expressions. Run a Python script by parsing the HTML to extract all the underlined expressions. This script lists them in a single spreadsheet (e.g., an Excel file).
extract_underlined_words.py2KB Delete duplicate expressions. Delete duplicates that appear in the spreadsheet. Only one occurrence of each expression is needed for re-coding purposes.
Sort the unique expressions in alphabetical order. This allows the grouping of expressions referring
to a common topic (with a common start, e.g., EU and EUROPEAN_UNION).
Standardise sentences. The purpose of re-coding is to standardise similar expressions, such as “Science Policies” and its derivatives will become “SCIENCE_POLICY“. To facilitate this step the column with the replacement value is automatically generated with spaces replaced by an underscore. The underscore character (‘_’) instructs Iramuteq to consider the phrase as a whole and prevent any splitting of the phrase into words. Consequently: change “SCIENCE POLICY” into “SCIENCE_POLICY.”
Convert to TXT format. Convert the Word document containing the entire corpus into TXT format. This is the format required by Iramuteq.
Prioritise expressions to be re-coded. This step replaces all expressions that have been re-coded manually in step 4.2. A Python script performs this substitution by replacing the longest expressions first. This avoids re-coding shorter expressions that would be included in larger ones, for example “COMMITTEE FOR SCIENCE POLICY“ must be recoded as “CSTP” before “SCIENCE POLICY” is re-coded as “SCIENCE_POLICY” (otherwise, the first expression would become “COMMITTEE FOR CSTP”, which is incorrect).
update_underlined_words.py1KB Warning. It is possible to repeat step 7.2 several times, when one realises that some extra expressions need to be recoded. In addition, one must pay attention to special characters in Iramuteq. For example, ampersands (&) are considered as spaces. For instance ‘S&T’ is indexed as the sequence ‘S’ followed by ‘T’, which is incorrect.
Lexicometric analysis. Once the re-coding is finished, open the TXT file with Iramuteq, selecting the language used in the corpus (English, for the OECD corpus). This protocol related to the pre-processing of a textual corpus ends here.