Utilizing deep learning models for the identification of enhancers and
  super-enhancers based on genomic and epigenomic features
Zahra Ahani Moein Shahiki Tash Yoel Ledo Mezquita Jason Angel
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.