This paper reviews major use cases for blockchain architectures relevant to the energy sector and continues with a critical review of issues to study in future research work including as related to energy consumption of blockchain architectures and ensuring a reliable distribution network and security of supply. It also reviews what is happening in the market with relation to smart contracts.

Abstract. We argue in this paper that in order to properly capture opinion and sentiment expressed in texts or dialogs any system needs a deep linguistic processing approach. As in other systems, we used ontology matching and concept search, based on standard lexical resources, but a natural language understanding system is still required to spot fundamental and pervasive linguistic phenomena. We implemented these additions to VENSES system and the results of the evaluation are compared to those reported in the state-of-the-art systems in sentiment analysis and opinion mining. We also provide a critical review of the current benchmark datasets as we realized that very often sentiment and opinion is not properly modeled.

Interaction mining is about discovering and extracting insightful information from digital conversations, namely those human-human information exchanges mediated by digital network technology. We present in this article a computational model of natural arguments and its implementation for the automatic argumentative analysis of digital conversations, which allows us to produce relevant information to build interaction business analytics applications overcoming the limitations of standard text mining and information retrieval technology. Applications include advanced visualisations and abstractive summaries.

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In this paper, we present our solution for argumentative analysis of call center conversations in order to provide useful insights for enhancing Customer Interaction Analytics to a level that will enable more qualitative metrics and key performance indicators (KPIs) beyond the standard approach used in Customer Interaction Analytics. These metrics rely on understanding the dynamics of conversations by highlighting the way participants discuss about topics. By doing that we can detect relevant situations such as social behaviors, controversial topics, customer oriented behaviors, and also predict customer satisfaction.

Abstract. Abstract summarization of conversations is a very challenging task that requires full understanding of the dialog turns, their roles and relationships in the conversations. We present an efficient system, derived from a fullyfledged text analysis system that performs the necessary linguistic analysis of turns in conversations and provides useful argumentative labels to build synthetic abstractive summaries of conversations.

The automated analysis of natural language data has become a central issue in the design of intelligent information systems. Processing unconstrained natural language data is still considered as an AI-hard task. However, various analysis techniques have been proposed to address specific aspects of natural language. In particular, recent interest has been focused on providing approximate analysis techniques, assuming that when perfect analysis is not possible, partial results may be still very useful.

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