LIA@CLEF 2018: Mining events opinion argumentation from raw unlabeled Twitter data using convolutional neural network

International audienceSocial networks on the Internet are becoming increasingly important in our society. In recent years, this type of media, through communication platforms such as Twitter, has brought new research issues due to the massive size of data exchanged and the important number of ever-increasing users. In this context, the CLEF 2018 Mining opinion argumentation task aims to retrieve, for a specific event (festival name or topic), the most diverse argumentative microblogs from a large collection of tweets about festivals in different languages. In this paper, we propose a four-step approach for extracting argumentative microblogs related to a specific query (or event) while no reference data is provided

Exploring Temporal Analysis of Tweet Content from Cultural Events

Abstract, Communication oraleInternational audienceOnline social networking platforms are an important commu- nication medium for cultural events, as they allow exchanging opinions almost in real-time, by publishing messages during the event itself, but also outside of this period. Word embedding has become a popular way to represent and extract information from such messages. In this paper, we propose a preliminary work aiming at assessing the benefits of taking temporal information into account when modeling messages in the context of a cultural event. We perform statistical and visual analyses on two different word representations: one including temporal information (Temporal Embedding), the second ignoring it (Word2Vec approach). Our preliminary results show that the obtained models exhibit some similarities, but also di er signi cantly in the way they represent certain speci c words. More interestingly, the temporal information conveyed by the Temporal Embedding model allows to identify more relevant word associations related to the domain at hand (cultural festivals)