@article {7045420,
	title = {Harvesting Opinions and Emotions from Social Media Textual Resources},
	journal = {Internet Computing, IEEE},
	volume = {19},
	number = {4},
	year = {2015},
	month = {July},
	pages = {46-50},
	keywords = {Adaptation models, Analytical models, Filtering, Internet/Web technologies, Media, Sentiment analysis, Text processing, textual resources, Web 2.0},
	issn = {1089-7801},
	doi = {10.1109/MIC.2015.28},
	author = {Despoina Chatzakou and Athena Vakali}
}
@inproceedings {6681459,
	title = {Micro-blogging Content Analysis via Emotionally-Driven Clustering},
	booktitle = {Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on},
	year = {2013},
	month = {Sept},
	pages = {375-380},
	keywords = {affective analysis methodology, Clustering algorithms, content management, content sharing, Dictionaries, emotion intensity monitoring, emotionally-driven clustering, Equations, human emotion states, information sharing, lexicon-based technique, Mathematical model, microblogging content analysis, pattern clustering, people perception, Pragmatics, Semantics, Sentiment analysis, social networking (online), social pulse, social relations, text analysis, Twitter},
	issn = {2156-8103},
	doi = {10.1109/ACII.2013.68},
	author = {Despoina Chatzakou and Vassiliki A. Koutsonikola and Athena Vakali and Konstantinos Kafetsios}
}
@inproceedings {conf/acii/TsagkalidouKVK11,
	title = {Emotional Aware Clustering on Micro-blogging Sources},
	booktitle = {ACII (1)},
	series = {Lecture Notes in Computer Science},
	volume = {6974},
	year = {2011},
	pages = {387-396},
	publisher = {Springer},
	organization = {Springer},
	abstract = {<p>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.</p>
},
	keywords = {Microblogging services, Sentiment analysis, web clustering},
	isbn = {978-3-642-24599-2},
	author = {Tsagkalidou, Katerina and Vassiliki A. Koutsonikola and Athena Vakali and Konstantinos Kafetsios},
	editor = {D{\textquoteright}Mello, Sidney K. and Graesser, Arthur C. and Schuller, Bj{\"o}rn and Martin, Jean-Claude}
}