This project integrating Alpaca-LoRA to fine-tune the LlaMA2 model revolutionizes how journalists decipher social media sentiment. This study collects real-time data from virtual currencies and forums and processes them to make them fit the needs of the model. Alpaca-LoRA steps in, optimizing the LlaMA2 model to grasp the nuances of social media discourse, amplifying its relevance in sentiment analysis. The model undergoes a meticulous fine-tuning process, absorbing key insights from the adapted framework. This cutting-edge methodology not only refines large language models but reshapes the approach to dissecting social media sentiments related to news events. Journalists now access a tool that swiftly decodes public reactions, aiding in crafting more resonant stories. Moreover, researchers find a goldmine for social studies, unraveling the intricate tapestry of societal responses to news through these refined sentiment analyses.
Our raw input variables: The historical time-series data of the monetary value of Aave, and the tweets with specific hashtags.
- Cryptocurrency Data: Our data set comprises daily market prices (AAVE–USD exchange rates) from Alpha Vantage API. We include open price, high price, low price, close price, and daily volume of AAVE from Oct 2020 to Dec 2022 in the dataset.
- Social Media Data: We collect tweets that contained the hashtag (#Aave/#AAVE) from Snscrape API. The timestamp is also from Oct 2020 to Dec 2022.