Detect predefined topics in a text - text-mining

Question

I would like to find in a text corpus (with long boring text), allusions about some pre-defined topic (let's say i am interested in the 2 topic: "Remuneration" and "Work condition").
For exemple finding in my corpus where (the specific paragraph) it is pointing problems about "remuneration".
To accomplish that i first thought about a deterministic approach: building a big dictionary, and thanks to regex maybe flagging those words in the text corpus. It is a very basic idea but i do not know how i could build efficiently my dictionary (i need a lot of words in the lexical field of remuneration). Do you know some website in french which could help me to build this dictionary ?
Perhaps can you think about a more clever approach based on some Machine Learning algorithm which could realize this task (i know about topic modelling but the difference here is that i am focusing on pre-determines subject/topic like "Remuneration"). I need a simple approach :)

Answer 1

The dictionary approach is a very basic one, but it could work. You can build the dictionary iteratively:
Suppose you want a dictionary of terms related to "work conditions".
Start with a seed, a small number of terms that may be related, with high probability, to work conditions.
Use this dictionary to run through the corpus and find relevant documents.
Now go through the relevant documents and find terms with high TFIDF value (terms which have high representation in the above documents but low representation in the rest of the corpus). These terms can be assumed to refer to the subject of "work conditions" as well.
Add the new terms you found to the dictionary.
Now you can run again through the corpus and find additional relevant documents.
You can repeat the above process for a pre-configured number of times, or until no more new terms are found.

Answer 2

A full treatment of such "topic analysis" problems is well beyond the scope of a Stack Overflow Q&A - There are multiple books and papers on such.
For a small, starter project: collect a number of articles which focus on discussing your topic(s), and on other topics. Rate each document according to whether or not it covers each of your chosen topics. Calculate the term-frequency-inverse-document-frequency for each of the sample articles. Convert these into a vector of the frequency of appearance of each word for each document. (You'll probably want to eliminate extremely common or ambiguous "stop words" from the analysis and do "stemming" as well. You can also scan for common sequences of two or more words.) This then defines a set of "positive" and "negative" examples for each defined topic.
If there's only a single topic of interest, you can then use a cosine-similarity function to determine which sample article is most like your new / input text sample. For multiple topics, you'll probably want to do something like Principal Component Analysis from the original text samples to identify which words and word combinations are most representative of each topic.
The quality of the classification will depend largely on the number of example texts you have to train the model and how much they differ.

Answer 3

If you're talking about the coding way of solving this, why don't you write a code (in your language) that finds the paragraph containing the word or the allusion word.
For example, I would do it like this in JavaScript
// longText is a long text that includes 4 paragraphs in total
const longText = `
In 1893, the first running, gasoline-powered American car was built and road-tested by the Duryea brothers of Springfield, Massachusetts. The first public run of the Duryea Motor Wagon took place on 21 September 1893, on Taylor Street in Metro Center Springfield.[32][33] The Studebaker Automobile Company, subsidiary of a long-established wagon and coach manufacturer, started to build cars in 1897[34]: p.66  and commenced sales of electric vehicles in 1902 and gasoline vehicles in 1904.[35]
In Britain, there had been several attempts to build steam cars with varying degrees of success, with Thomas Rickett even attempting a production run in 1860.[36] Santler from Malvern is recognized by the Veteran Car Club of Great Britain as having made the first gasoline-powered car in the country in 1894,[37] followed by Frederick William Lanchester in 1895, but these were both one-offs.[37] The first production vehicles in Great Britain came from the Daimler Company, a company founded by Harry J. Lawson in 1896, after purchasing the right to use the name of the engines. Lawson's company made its first car in 1897, and they bore the name Daimler.[37]
In 1892, German engineer Rudolf Diesel was granted a patent for a "New Rational Combustion Engine". In 1897, he built the first diesel engine.[1] Steam-, electric-, and gasoline-powered vehicles competed for decades, with gasoline internal combustion engines achieving dominance in the 1910s. Although various pistonless rotary engine designs have attempted to compete with the conventional piston and crankshaft design, only Mazda's version of the Wankel engine has had more than very limited success.
All in all, it is estimated that over 100,000 patents created the modern automobile and motorcycle.
`
document.querySelector('.searchbox').addEventListener('submit', (e)=> { e.preventDefault(); search() })
function search(){
const allusion = document.querySelector('#searchbox').value.toLowerCase()
const output = document.querySelector('#results-body ol')
output.innerHTML = "" // reset the output
const paragraphs = longText.split('\n').filter(item => item != "")
const included = paragraphs.filter((paragraph) => paragraph.toLowerCase().includes(allusion))
let foundIn = included.map(paragraph => `<div class="result-row"> <li>${paragraph.toLowerCase()}</li>
</div>`)
foundIn = foundIn.map(el => el.replaceAll(allusion, `<span class="highlight">${allusion}</span>`))
output.insertAdjacentHTML('afterbegin', foundIn.join('\n'))
}
.container{
padding : 5px;
border: .2px solid black;
}
.searchbox{
padding-bottom: 5px
}
.searchbox input {
width: 90%
}
.result-row{
padding-bottom: 5px;
}
.highlight{
background: yellow;
}
h3 span {
font-size: 14px;
font-style: italic;
}
<div class="container">
<form class="searchbox">
<h3>Give me an hint: <span>ex: car, gasoline, company</span> </h3>
<input id="searchbox" type="text" placeholder="allusion word, ex: car, gasoline, company">
<button type"submit"> find </button>
</form>
<div id="results-body">
<ol></ol>
</div>
</div>