No Sentiment is an Island

BOBICEV, Victoria; SOKOLOVA, Marina

dc.contributor.author	BOBICEV, Victoria
dc.contributor.author	SOKOLOVA, Marina
dc.date.accessioned	2021-04-10T15:09:20Z
dc.date.available	2021-04-10T15:09:20Z
dc.date.issued	2015
dc.identifier.citation	BOBICEV, Victoria, SOKOLOVA, Marina. No Sentiment is an Island. In: Discovery Science: proc. of 18th International Conference, DS 2015 4-6 Oct. 2015, Banff, AB, Canada, 2015, V. 9356 pp. 180-187. ISBN 978-3-319-24282-8.	en_US
dc.identifier.isbn	978-3-319-24282-8
dc.identifier.uri	https://doi.org/10.1007/978-3-319-24282-8_4
dc.identifier.uri	http://repository.utm.md/handle/5014/14096
dc.description	Acces full text: https://doi.org/10.1007/978-3-319-24282-8_4	en_US
dc.description.abstract	In this study we propose a new method to classify sentiments in messages posted on online forums. Traditionally, sentiment classification relies on analysis of emotionally-charged words and discourse units found in the classified text. In coherent online discussions, however, messages’ non-lexical meta-information can be sufficient to achieve reliable classification results. Our empirical evidence is obtained through multi-class classification of messages posted on a medical forum.	en_US
dc.language.iso	en	en_US
dc.publisher	Springer Nature Switzerland	en_US
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	sentiment analysis	en_US
dc.subject	binary feature	en_US
dc.subject	sentiment classification	en_US
dc.subject	emotional communication	en_US
dc.subject	medical forums	en_US
dc.subject	forums	en_US
dc.title	No Sentiment is an Island	en_US
dc.type	Article	en_US