Every day, more and more people harness the power of social media platforms to express their thoughts, share information and personal experiences, and engage with others. All this knowledge can then be transformed into informative reports with the …
Every day, many people use social media platforms to share information, thoughts, narratives and personal experiences. The vast volume of user-generated content offers valuable insights into the latest news and trends but also poses serious …
In this post I show how to leverage BERT, a transformer-based language representation model, in order to identify the personality type of users based on their writing style and the content of their posts, according to the Myers-Briggs indicator (MBTI).
In what follows, I'll show how to fine-tune a BERT classifier, using Huggingface and Keras+Tensorflow, for dealing with two different text classification problems. The first consists in detecting the sentiment (*negative* or *positive*) of a movie review, while the second is related to the classification of a comment based on different types of toxicity, such as *toxic*, *severe toxic*, *obscene*, *threat*, *insult* and *identity hate*.