<?xml version="1.0" encoding="UTF-8"?><xml><records><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%">Radka Nacheva</style></author><author><style face="normal" font="default" size="100%">Snezhana Sulova</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Research on the Overall Attitude Towards Mobile Learning in Social Media: Emotions Mining Approach</style></title><secondary-title><style face="normal" font="default" size="100%">DIGILIENCE 2019</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">emotions mining</style></keyword><keyword><style  face="normal" font="default" size="100%">higher education</style></keyword><keyword><style  face="normal" font="default" size="100%">mobile learning</style></keyword><keyword><style  face="normal" font="default" size="100%">social media</style></keyword><keyword><style  face="normal" font="default" size="100%">text mining</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2-4 October</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Sofia, Bulgaria</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper, we address the importance of classification and social media mining of human emotions. We compared different theories about basic emotions and the application of emotion theory in practice. Based on Plutchik&amp;#39;s classification, we suggest creating a specialized lexicon with terms and phrases to identify emotions for research of general attitudes towards mobile learning in social media. The approach can also be applied to other areas of scientific knowledge that aim to explore the emotional attitudes of users in social media. It is based on the Natural Language Processing and more specifically uses text mining classification algorithms. For test purposes, we have retrieved a number of tweets on users&amp;#39; attitudes towards mobile learning.&lt;/p&gt;&lt;p&gt;This paper is included in the program of &lt;a href=&quot;https://digilience.org&quot;&gt;DIGILIENCE 2019&lt;/a&gt; and will be published in the post-conference volume. In the meantime, you can download the presentation using the link above.&lt;/p&gt;</style></abstract></record></records></xml>