The AI Sentiment Analysis tool will collect data from Twitter and various news sources, analyze the sentiment in the tweets or news articles, and then rate each source on a scale ranging from fearful to enthusiastic.
Based on collected historical data, it will correlate the price movement of different tokens to the sentiment scores. As more and more data is collected (both historical and current), the tool will become increasingly accurate at predicting price movements based on the complex interplay between sentiment and the market.
When it comes to Twitter sentiment analysis, the AI will also look at profiles that can have a significant effect on the price of an asset with their tweets (e.g. Elon Musk), as well as the number of likes, retweets and comments that a tweet receives, in order to determine the sentiment score. Based on the overall sentiment of the tweets about a specific token in a given time period, it will calculate an average sentiment score and predict the price movement based on previous correlations, and the same applies to news articles.
In later updates, the AI will also be able to transcribe spoken words into text, allowing it to listen to the news to analyze sentiment there as well, as this is typically the first source of information for the biggest tokens and the market as a whole. It will also add more complex variables, such as the tone of the voice of the person speaking, and it will take this into consideration for the prediction as well.