Cryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments. After recent events during which GameStop’s stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market. The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting. A popular integration technique regards representing social media opinions via sentiment features. However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists. We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-of-the-art sentiment and emotion recognition. The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domain-related subReddits along with Bitcoin’s financial data. We evaluate several widely adopted neural architectures for crypto price change forecasting. Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques. We make our dataset publicly available for research.

The successful application of argument mining in the legal domain can dramatically impact many disciplines related to law. For this purpose, we present Demosthenes, a novel corpus for argument mining in legal documents, composed of 40 decisions of the Court of Justice of the European Union on matters of fiscal state aid. The annotation specifies three hierarchical levels of information: the argumentative elements, their types, and their argument schemes. In our experimental evaluation, we address 4 different classification tasks, combining advanced language models and traditional classifiers.

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain.

Creating balanced labeled textual corpora for complex tasks, like legal analysis, is a challenging and expensive process that often requires the collaboration of domain experts.To address this problem, we propose a data augmentation method based on the combination of GloVe word embeddings and the WordNet ontology.We present an example of application in the legal domain, specifically on decisions of the Court of Justice of the European Union.Our evaluation with human experts confirms that our method is more robust than the alternatives.