Since an update to the Sentistock bot was only recently made available, which now makes decisions on Bitcoin much faster than before – every 15 minutes (CryptoPulse strategy), compared to the earlier hourly one (Conservative strategy), it seems an opportune time to compare how the bot would have fared using the above strategies over the course of this year –
Recently, the so-called generative artificial intelligence (GenAI) has received great attention. It is capable of generating new data, images, text or other content based on patterns or input, to quote ChatGPT. Or put another way, it can create new data such as text, images, videos, and music. It relies on machine learning algorithms that allow it to analyze existing data
Two bots were given the roles of investors. They used two different emotion-based investment strategies: a conservative one and a dynamic one. We analysed the results of their actions relative to the price of the bitcoin that was traded, the rate of return and the risk that each investment entailed. An important caveat: the bot makes a choice every hour
8 basic emotions. Why so many? In this read we want you to meet the idea behind the unique solution that we came up with. It works wider than popular sentiment analysis tools used today for monitoring internet activities and more. Why? Instead of measuring sentiment, we focus on emotions. What does the emotion analysis offer that sentiment analysis does
Sentimenti = emotions. We have already written how to analyse emotions correctly whilst having a way to automatically measure or classify them. Today we will tell you how we have collected data to create Sentimenti tools. So, once again, we are writing about how to do something right – to research the emotional meaning of texts. The text was written
Sentimenti tools! Dr Jan Kocoń is a natural language engineer and the person behind the machine learning process within SentiTool, our solution for analyzing emotions in the text. Dr Kocoń coordinates the work of the linguistics team, integrates individual elements of the tool, and works closely with the IT team. If you have to describe Sentimenti and the tools to
Since an update to the Sentistock bot was only recently made available, which now makes decisions on Bitcoin much faster than before – every 15 minutes (CryptoPulse strategy), compared to the earlier hourly one (Conservative strategy), it seems an opportune time to compare how the bot would have fared using the above strategies over the course of this year –
Recently, the so-called generative artificial intelligence (GenAI) has received great attention. It is capable of generating new data, images, text or other content based on patterns or input, to quote ChatGPT. Or put another way, it can create new data such as text, images, videos, and music. It relies on machine learning algorithms that allow it to analyze existing data
Two bots were given the roles of investors. They used two different emotion-based investment strategies: a conservative one and a dynamic one. We analysed the results of their actions relative to the price of the bitcoin that was traded, the rate of return and the risk that each investment entailed. An important caveat: the bot makes a choice every hour
8 basic emotions. Why so many? In this read we want you to meet the idea behind the unique solution that we came up with. It works wider than popular sentiment analysis tools used today for monitoring internet activities and more. Why? Instead of measuring sentiment, we focus on emotions. What does the emotion analysis offer that sentiment analysis does
Sentimenti = emotions. We have already written how to analyse emotions correctly whilst having a way to automatically measure or classify them. Today we will tell you how we have collected data to create Sentimenti tools. So, once again, we are writing about how to do something right – to research the emotional meaning of texts. The text was written
Sentimenti tools! Dr Jan Kocoń is a natural language engineer and the person behind the machine learning process within SentiTool, our solution for analyzing emotions in the text. Dr Kocoń coordinates the work of the linguistics team, integrates individual elements of the tool, and works closely with the IT team. If you have to describe Sentimenti and the tools to