In recent years, social media has emerged as one of the main platforms for real-time reporting of issues during disasters and catastrophic events. While great strides have been made in collecting such information, there remains an urgent need to …
In the digital landscape, social media has emerged as a prevalent channel for global communication, connecting like-minded individuals worldwide. However, while facilitating information exchange, it is also susceptible to the dissemination of false …
In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by …
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*.