This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our primary objective was to conduct a psycholinguistic and emotion analysis of social media content associated with these cryptocurrencies. To enable investigators to make more informed decisions. The study involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns that emerge within each coin's community. To achieve this, we utilized advanced text analysis techniques. Additionally, our work unveiled an intriguing Understanding of the interplay between these digital assets within the cryptocurrency community. By examining which coin pairs are mentioned together most frequently in the dataset, we established correlations between different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from Twitter. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable Perception into the linguistic nuances of various digital coins' online communities and provides a deeper understanding of their interactions in the cryptocurrency space.

翻译：本文对涵盖九种广泛认可的加密货币（具体为Cardano、Binance、Bitcoin、Dogecoin、Ethereum、Fantom、Matic、Shiba和Ripple）的大型英语推文数据集进行了全面分析。我们的主要目标是对与这些加密货币相关的社交媒体内容进行心理语言学和情感分析，以便调查人员能够做出更明智的决策。研究通过比较不同数字货币的语言特征，揭示了每种货币社区中独特的语言模式。为此，我们采用了先进的文本分析技术。此外，我们的工作揭示了加密货币社区中这些数字资产之间相互作用的有趣理解。通过检查数据集中哪些货币对最常被同时提及，我们建立了不同加密货币之间的相关性。为确保研究结果的可靠性，我们最初从Twitter收集了832,559条推文。这些推文经过严格的预处理阶段，最终形成了一个包含115,899条推文的精炼数据集用于分析。总体而言，我们的研究为各种数字货币在线社区的语言细微差别提供了宝贵见解，并更深入地理解了它们在加密货币空间中的互动。