<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Konstantinos Kafetsios</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detecting Variation of Emotions in Online Activities</style></title><secondary-title><style face="normal" font="default" size="100%">Expert Systems with Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Emotion detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Hybrid process</style></keyword><keyword><style  face="normal" font="default" size="100%">Lexicon-based approach</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0957417417305213</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">89</style></volume><pages><style face="normal" font="default" size="100%">318 - 332</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Online text sources form evolving large scale data repositories out of which valuable knowledge about human emotions can be derived. Beyond the primary emotions which refer to the global emotional signals, deeper understanding of a wider spectrum of emotions is important to detect online public views and attitudes. The present work is motivated by the need to test and provide a system that categorizes emotion in online activities. Such a system can be beneficial for online services, companies recommendations, and social support communities. The main contributions of this work are to: (a) detect primary emotions, social ones, and those that characterize general affective states from online text sources, (b) compare and validate different emotional analysis processes to highlight the most efficient, and (c) provide a proof of concept case study to monitor and validate online activity, both explicitly and implicitly. The proposed approaches are tested on three datasets collected from different sources, i.e., news agencies, Twitter, and Facebook, and on different languages, i.e., English and Greek. Study results demonstrate that the methodologies at hand succeed to detect a wider spectrum of emotions out of text sources.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Konstantinos Kafetsios</style></author><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Nikolaos Tsigilis</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Experience of emotion in face to face and computer-mediated social interactions: An event sampling study</style></title><secondary-title><style face="normal" font="default" size="100%">Computers in Human Behavior</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer-mediated communication</style></keyword><keyword><style  face="normal" font="default" size="100%">Emotion</style></keyword><keyword><style  face="normal" font="default" size="100%">FtF</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Social interaction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0747563217304557</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">76</style></volume><pages><style face="normal" font="default" size="100%">287 - 293</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The present study compared the experience of emotion in social interactions that take place face to face (FtF), co-presently, and those that take place online, in computer-mediated communications (CMC). For a period of ten days participants reported how intensely they experienced positive and negative emotions in CMC and in FtF interactions they had with persons from their social network. Results from factor analyses discerned a three factor emotion structure (positive, negative, and anxious emotions) that was largely shared between CMC and FtF social interactions. Multilevel analyses of emotion across modes of interaction found that in FtF social encounters participants experienced more positive and less negative emotion and higher satisfaction than in CMC; there was no difference in anxious emotion. Positive, but not negative emotions or anxiety partially mediated levels of satisfaction differences between interactions in CMC and those taking place FtF. The results point to similarities and differences in emotion experience in FtF and CMC, underlining in particular the affiliative function of positive emotion in peoples' encounters.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Despoina Chatzakou</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Konstantinos Kafetsios</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Micro-blogging Content Analysis via Emotionally-Driven Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">affective analysis methodology</style></keyword><keyword><style  face="normal" font="default" size="100%">Clustering algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">content management</style></keyword><keyword><style  face="normal" font="default" size="100%">content sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">Dictionaries</style></keyword><keyword><style  face="normal" font="default" size="100%">emotion intensity monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">emotionally-driven clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Equations</style></keyword><keyword><style  face="normal" font="default" size="100%">human emotion states</style></keyword><keyword><style  face="normal" font="default" size="100%">information sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">lexicon-based technique</style></keyword><keyword><style  face="normal" font="default" size="100%">Mathematical model</style></keyword><keyword><style  face="normal" font="default" size="100%">microblogging content analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">people perception</style></keyword><keyword><style  face="normal" font="default" size="100%">Pragmatics</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">Sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">social networking (online)</style></keyword><keyword><style  face="normal" font="default" size="100%">social pulse</style></keyword><keyword><style  face="normal" font="default" size="100%">social relations</style></keyword><keyword><style  face="normal" font="default" size="100%">text analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Twitter</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">375-380</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tsagkalidou, Katerina</style></author><author><style face="normal" font="default" size="100%">Vassiliki A. Koutsonikola</style></author><author><style face="normal" font="default" size="100%">Athena Vakali</style></author><author><style face="normal" font="default" size="100%">Konstantinos Kafetsios</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">D’Mello, Sidney K.</style></author><author><style face="normal" font="default" size="100%">Graesser, Arthur C.</style></author><author><style face="normal" font="default" size="100%">Schuller, Björn</style></author><author><style face="normal" font="default" size="100%">Martin, Jean-Claude</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Emotional Aware Clustering on Micro-blogging Sources</style></title><secondary-title><style face="normal" font="default" size="100%">ACII (1)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Microblogging services</style></keyword><keyword><style  face="normal" font="default" size="100%">Sentiment analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">web clustering</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6974</style></volume><pages><style face="normal" font="default" size="100%">387-396</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-24599-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Microblogging services have nowadays become a very popularcommunication tool among Internet users. Since millions of usersshare opinions on different aspects of life everyday, microblogging websites are considered as a credible source for exploring both factual and subjective information. This fact has inspired research in the area of automatic sentiment analysis. In this paper we propose an emotional aware clustering approach which performs sentiment analysis of users tweets onthe basis of an emotional dictionary and groups tweets according to the degree they express a specific set of emotions. Experimental evaluations on datasets derived from Twitter prove the efficiency of the proposed approach.&lt;/p&gt;
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