Exploring concept drift in technology by tweets mining

Over the last decade, a dramatic transform happened in information sources and their use in the digital era. Social media networks have brought a new way of expressing the sentiments of individuals. The matter went beyond being an expression of separate opinions of some individuals, as companies, official institutions and various organizations have pages on the communication sites through which they share various developments, products, opinions, and sometimes even official decisions. Social media has evolved into a medium with a vast quantity of information, allowing users to access the opinions of other users, which can be classified into several sentiment categories, and are gradually taking a crucial role in decision making. Twitter is a microblogging service built to describe what is happening anywhere worldwide, at any moment. It's a fascinating forum for more than 500M messages per day from about 1.3 billion people. Twitter data is short, specific, and easily accessible, that's why it has become one of the best sources for sentimental analysis and knowledge discovery by data streams mining. The fact that the underlying distribution of data may vary over time, resulting in the phenomena of concept drift, which is one of the main problems that affects data streams mining. In this study, we present an approach to explore and understand the concept drift occurring in Twitter data streams. Two machine learning technique Naive Bayes Classifier and eXtreme Gradient Boosting (XGBoost) Classifier were applied on more than 11K tweets focused on two technology products (iPhone 13 & iPhone 14), and to detect / understand concept drift and specify whether concept drift in a technology area is a radical or an incremental innovation