## 3 Materials and Methods
### Data Gathering, Processing, Extraction, and Analysis
For example, it is possible to find noun phrases that follow specific verbs (e.g., \"require\", \"seek\", and \"need\") or adjectives (e.g., \"strong\", \"excellent\", and \"demonstrated\"). Another way to extract the required data from the text is regex. Regular expressions, abbreviated as regex, are powerful for pattern matching and text processing. A regex is a string of characters defining a specific search pattern. Regular expressions provide a concise and flexible way to extract information from unstructured data, such as log files, emails, and web pages. They can be implemented in various applications, including data validation, text extraction, and cleaning. While regular expressions can be complex and challenging to master, they are an essential tool for data scientists and software developers who need to work with copious amounts of text data. In this case, using regex makes it possible to find the requirements in job advertisements using patterns such as \"Requirements\" and the ending pattern \"Company offers\". For example:  
_\"This is the job description. Requirements: experience, English etc. Company offers: health insurance, flexible work\"._  
After using regex and the initial pattern \"Requirements\" and the final pattern \"Company offers\", the text \"experience, English etc.\" is then extracted. Another particularly developing way to extract the necessary information, systematize substantial amounts of textual information, or even generate additional textual data is large language models. LLMs are advanced machine learning models that are capable of processing and generating human-like language. These models are trained on massive amounts of textual data, such as books, articles, and websites, using complex algorithms that enable them to identify and learn patterns in language. Some examples of LLMs include OpenAI's GPT-4 (OpenAI GPT-4 information: [[https://openai.com/product/gpt-4](https://openai.com/product/gpt-4)]([https://openai.com/product/gpt-4](https://openai.com/product/gpt-4)), accessed on 25 March 2023), GPT-3.5-turbo (OpenAI GPT-3.5-turbo information: [[https://platform.openai.com/docs/models/gpt-3-5](https://platform.openai.com/docs/models/gpt-3-5)]([https://platform.openai.com/docs/models/gpt-3-5](https://platform.openai.com/docs/models/gpt-3-5)), accessed on 25 March 2023), GPTNeoX [33], and Google's BERT [31]. LLMs have many applications, including in natural language processing, machine translation, and chatbot development. They are becoming increasingly popular in artificial intelligence and have the potential to revolutionize how humans interact with computers and machines. Using the above example and extracting the necessary data using this methodology, the request looks like this:  
_\"Extract job requirements from the text provided:  
Figure 2: The ideal structure of the unstructured job advertisement text. _This is the job description requirements: experience, English etc. The company offers health insurance and flexible work\"._  
The results obtained after using OpenAI's GPT-3.5-turbo are:  
_\"Based on the text provided, the job requirements are experience (specifics not mentioned) English proficiency (specifics not mentioned). It is important to note that without further context, it is difficult to determine the level of experience or proficiency required for the job\"._  
At the time of research, the price of GPT-3.5-turbo was $0.002 per 1000 tokens, where 1000 tokens are about 750 words. The results obtained after using Open-AI's GPT-4 look like this: _\"From the provided text, the job requirements are 1. Experience 2. English proficiency. Additionally, the company offers: 1. Health insurance, 2. Flexible work\"._ At the time of research, GPT-4 was $0.03 per 1000 prompt tokens and $0.06 per 1000 completion tokens, making it even about 15 times more expensive. Given that the collected data contained about 120 million words and the answers' size would also be similar, the required number of tokens would be 320 million.