Background: Textual data analysis has become a popular method for examining complex human behavior in various fields, including psychology, psychiatry, sociology, computer science, data mining, forensic sciences, and communication studies. However, identifying the most relevant textual parameters for analyzing complex behavior is still a challenge.
Goal of Study: This paper aims to explore potential textual parameters that could be useful in analyzing behavior through complex textual data. Furthermore, we have examined the randomly generated text based on different textual parameters.
Methods: To achieve this goal, we conducted a comprehensive review of the literature on textual data analysis and identified several potential topics that could be relevant, such as sentiment analysis, discourse analysis, lexical analysis, and syntactic analysis. We discuss the theoretical background and practical implications of each parameter and provide examples of how they have been used in previous research. Furthermore, we highlight the importance of considering the context in which these parameters are applied and the need for interdisciplinary collaboration to gain a deeper understanding of complex behavior through textual data analysis. Furthermore, we have provided Python code in the Supplementary Materials to facilitate a comprehensive analysis of such behaviors. In addition, to generate the text for analysis, we utilized ChatGPT 3.5 Turbo by requesting it to generate a random text of 1000 words divided into five paragraphs. Afterwards, we applied the provided Python code to analyze the randomly generated text.
Conclusion: Overall, this paper provides a foundation for researchers to identify relevant textual parameters to analyze complex human behavior in their respective fields such as linguistics, sociology, psychiatry, and psychology.



中文翻译：

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探索文本参数与心理和认知因素之间的关联

背景：文本数据分析已成为各个领域检查复杂人类行为的流行方法，包括心理学、精神病学、社会学、计算机科学、数据挖掘、法医学和传播研究。然而，识别用于分析复杂行为的最相关的文本参数仍然是一个挑战。
研究目标：本文旨在探索潜在的文本参数，这些参数可用于通过复杂的文本数据分析行为。此外，我们还检查了基于不同文本参数的随机生成的文本。
方法：为了实现这一目标，我们对文本数据分析的文献进行了全面的回顾，并确定了几个可能相关的潜在主题，例如情感分析、话语分析、词汇分析和句法分析。我们讨论了每个参数的理论背景和实际意义，并提供了如何在之前的研究中使用它们的示例。此外，我们强调考虑应用这些参数的背景的重要性，以及跨学科合作的必要性，以通过文本数据分析更深入地了解复杂行为。此外，我们在补充材料中提供了 Python 代码，以便于对此类行为进行全面分析。此外，为了生成用于分析的文本，我们利用 ChatGPT 3.5 Turbo，要求它生成 1000 个单词的随机文本，分为 5 个段落。之后，我们应用提供的 Python 代码来分析随机生成的文本。
结论：总的来说，本文为研究人员识别相关文本参数以分析语言学、社会学、精神病学和心理学等各自领域的复杂人类行为奠定了基础。