Abstract:
: Decentralized Web (Web3) and Finance (DeFi) have become the main discussion topic in research and industry fields.
Cryptocurrencies, as an essential part of DeFi, enjoyed the interest of many stakeholders such as companies, professionals, researchers,
and even common citizens eager to benefit from the proposed ecosystems. Although previous research studies focused on establishing
price prediction systems using Sentiment Analysis (SA) techniques, the main focus of these studies was the performance of the
predictive system rather than the accuracy and efficiency of the used models. In our work, we address two research questions; the
predictability of cryptocurrency price based on past social and technical information, and the effect of social features on cryptocurrency
price fluctuations using an SA and a Time Series approach. A combination of selected social and technical features was processed
and reframed as a prediction problem, then studied to assess the ability of our model to predict the desired price. We noted that there
is both an explicit correlation for some considered features and implicit for others, also social features including overall positive and
neutral sentiment, and community engagement improved the performance of our model.
