SRIS 2016 Abstracts

Short Papers
Paper Nr: 3

Ontological Interaction Modeling and Semantic Rule-based Reasoning for User Interface Adaptation


Fatma-Zohra Lebib, Hakima Mellah and Linda Mohand-Oussaid

Abstract: The paper aims to show how reasoning on ontology can be helpful for user interface adaptation. From a set of user characteristics and interface parameters, it is possible to deduct the most suitable and adaptable interfaces for him/her. To do so, Semantic Web Rule Language (SWRL) rules are used to derive the appropriate interface for a specific user, considering different factors related to his/her abilities, preferences, skills, etc. A use case, in handicrafts domain, is presented; different input and output interaction modalities (writing, selection, text, speech, etc) are proposed to a handcraft woman according to her sensory perception and motor skills. The modalities are structured within what we called "interaction ontology".

Paper Nr: 4

Integrating User’s Emotional Behavior for Community Detection in Social Networks


Andreas Kanavos, Isidoros Perikos, Ioannis Hatzilygeroudis and Athanasios Tsakalidis

Abstract: The analysis of social networks is a very challenging research area. A fundamental aspect concerns the detection of user communities, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Detecting communities is of great importance in sociology, biology as well as computer science where systems are often represented as graphs. In this paper we present a novel methodology for community detection based on users’ emotional behavior. The methodology analyzes user’s tweets in order to determine their emotional behavior in Ekman emotional scale. We define two different metrics to count the influence of produced communities. Moreover, the weighted version of a modularity community detection algorithm is utilized. Our results show that our proposed methodology creates influential enough communities.

Paper Nr: 5

A Classifier Ensemble Approach to Detect Emotions Polarity in Social Media


Isidoros Perikos and Ioannis Hatzilygeroudis

Abstract: The advent of social media has changed completely the role of the users and has transformed them from simple passive information seekers to active producers. The user generated textual data in social media and microblogging platforms are rich in emotions, opinions and attitudes and necessitate automated methods to analyse and extract knowledge from them. In this paper, we present a classifier ensemble approach to detect emotional content in social media and examine its performance under bagging and boosting combination methods. The classifier ensemble aims to take advantage of the base classifiers’ benefits and constitutes a promising approach to detect sentiments in social media. Our classifier ensemble combines a knowledge based tool that performs deep analysis of the natural language and two machine learning classifiers, a Naïve Bayes and a Maximum Entropy which are trained on ISEAR and Affective text datasets. The evaluation study conducted revealed quite promising results and indicates that the ensemble classifier approach can improve the performance of sole classifiers on emotion detection in Twitter and that the boosting seems to be more suitable and to perform better than bagging.