A thorough explanation of how to use ChatGPT for text mining

Text mining using ChatGPT: Efficient data analysis

Text mining, which extracts useful information from a vast amount of unstructured data, is dramatically streamlined using AI technologies such as ChatGPT. In this article, we will explain the text mining method using ChatGPT, with specific examples. Master a variety of approaches, including document summaries, keyword occurrence rate analysis, and user review classification, and aim to improve the efficiency of data analysis.

table of contents

• What is text mining?
• Text mining using ChatGPT: Practical examples
  • A summary of the long sentence
  • Keyword occurrence rate analysis
  • User review classification (Using Advanced Data Analysis/Code Interpreter)
  • Classification of user reviews (spreadsheet integration)
• summary

What is text mining?

Text mining is a technology that extracts valuable information from a large number of documents based on natural language processing (NLP). Through the analysis of unstructured text data, we uncover hidden patterns and trends, contributing to research and business.

Text mining using ChatGPT: Practical examples

Here we will introduce practical examples of text mining using ChatGPT, using the Ministry of Internal Affairs and Communications' AI Network Society Promotion Council Report (2022).

A summary of the long sentence

ChatGPT's high natural language processing power makes it easy to summarize long sentences. Clear instructions (prompts) are important for effective summary. By giving specific instructions such as "Summary this sentence in five sentences" and "Show three important points in bullet points," ChatGPT generates a precise summary. As an example, ChatGPT's ability to instantly summarise a 127-page PDF will greatly improve the efficiency of data analysis.

Actual interaction (summary in summary)

Keyword occurrence rate analysis

The frequency of occurrence of specific keywords and phrases in text data is essential for understanding data trends. ChatGPT will respond promptly to questions such as, "How many times does "XX" appear in this text?" For example, you can assess the importance of a topic by examining the number of times an AI Development Guideline appears.

Actual interaction (checking the appearance rate of words)

User review classification (Using Advanced Data Analysis/Code Interpreter)

ChatGPT's Advanced Data Analysis (formerly Code Interpreter) helps in analyzing and classifying user reviews. In an example using review data for fictional Italian restaurants provided in Excel files, ChatGPT categorized them into emotions (positive/negative/neutral) and categories (taste, appearance, customer service, etc.). However, since the ChatGPT standard library has issues with compatibility with Japanese, we provide an example of analysis in English. (Japanese analysis is also possible if you use a Japanese-compatible library)

Response when trying to analyze emotions in Japanese

Data before analysis

ChatGPT analysis

Data after analysis

Classification of user reviews (spreadsheet integration)

By using extensions such as "GPT for Sheets and Docs", spreadsheets and ChatGPT can be linked to text mining. By setting the API key and entering functions, you can perform the same analysis as Advanced Data Analysis. For more information, please refer to the related article.

summary

ChatGPT is a powerful tool to streamline text mining. Its advanced natural language processing capabilities allow for rapid and accurate data analysis. Please use the techniques introduced in this article to try text mining using ChatGPT.

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