@inproceedings{tang-chen-2012-mining,
title = "Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition",
author = "Tang, Yi-jie and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/117_Paper.pdf",
pages = "1226--1229",
abstract = "The conversations between posters and repliers in microblogs form a valuable writer-reader emotion corpus. This paper adopts a log relative frequency ratio to investigate the linguistic features which affect emotion transitions, and applies the results to predict writers' and readers' emotions. A 4-class emotion transition predictor, a 2-class writer emotion predictor, and a 2-class reader emotion predictor are proposed and compared.",
}
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<abstract>The conversations between posters and repliers in microblogs form a valuable writer-reader emotion corpus. This paper adopts a log relative frequency ratio to investigate the linguistic features which affect emotion transitions, and applies the results to predict writers’ and readers’ emotions. A 4-class emotion transition predictor, a 2-class writer emotion predictor, and a 2-class reader emotion predictor are proposed and compared.</abstract>
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%0 Conference Proceedings
%T Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition
%A Tang, Yi-jie
%A Chen, Hsin-Hsi
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 may
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F tang-chen-2012-mining
%X The conversations between posters and repliers in microblogs form a valuable writer-reader emotion corpus. This paper adopts a log relative frequency ratio to investigate the linguistic features which affect emotion transitions, and applies the results to predict writers’ and readers’ emotions. A 4-class emotion transition predictor, a 2-class writer emotion predictor, and a 2-class reader emotion predictor are proposed and compared.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/117_Paper.pdf
%P 1226-1229
Markdown (Informal)
[Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition](http://www.lrec-conf.org/proceedings/lrec2012/pdf/117_Paper.pdf) (Tang & Chen, LREC 2012)
ACL