Revealing Emotional Insights From Mental Health Discussions on Instagram and TikTok Using BERT Models

The research addresses challenges related to mental health issues in social media by integrating natural language processing. First, the study extends a previous corpus labelled with emotions and polarity by including new Instagram and TikToK posts related to celebrity and influencer disclosures about mental health. This corpus is the first Spanish corpus designed to analyse the impact of social responses to mental health narratives on two of the most widely used social networks. Secondly, the research integrates BERT (Bidirectional Encoder Representations) classification models to improve emotion and polarity detection. One of the modelled algorithms, MenTaiBERT, leveraging a specialised classification layer demonstrates superiority over the other BERT algorithms, achieving 99% accuracy in emotion detection and 98% accuracy in polarity. Indeed, MenTaiBERT significantly outperforms the accuracy of the other algorithms by up to 13 percentage points. Third, A user-friendly graphical tool has been designed, based on the previous corpus and classification models, to help practitioners identify emotional patterns in social media posts related to mental health. In summary, analysing through innovative artificial intelligence strategies the emotional impact of celebrity posts on social networks is crucial, especially among young people, as these platforms significantly influence their self-esteem, perception of reality and emotional well-being.