Natural Language Processing tasks have been widely studied in the context of big corpora, but, when applied to social media content, they have been proven to be harder. This is mainly due to the following reasons:

• Social media have a high-paced, conversational and idiosyncratic nature;
• There might be restriction in the number of characters allowed (this is the case on Twitter);
• Short texts contains a limited amount of contextual cues;

The lack of a unified evaluation framework makes it hard to compare different models.

In this project I used BERTweet, a large-scale language model pretrained on English tweets, and I evaluated its performances on three different NLP tasks:

• Emotion Recognition, which consists in recognizing the emotion evoked in a text (a tweet in this case);
• Hate Speech Detection, which consists in understanding whether a tweet is hateful or not towards certain target communities;
• Sentiment Analysis, i.e. the task of recognizing if the content of a text is positive, negative or neutral.

The colorization of greyscale images is an ill-posed problem that was approached in different ways in literature. This project provides a comparative analysis concerning five pre-trained colorization models and a cartoonization-based baseline of our invention. The performances are assessed through both quantitative and qualitative metrics, with a final evaluation of the results with respect to image filtering.