@inproceedings{simeonova-2017-gradient,
title = "Gradient Emotional Analysis",
author = "Simeonova, Lilia",
editor = "Kovatchev, Venelin and
Temnikova, Irina and
Gencheva, Pepa and
Kiprov, Yasen and
Nikolova, Ivelina",
booktitle = "Proceedings of the Student Research Workshop Associated with {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/issn.1314-9156.2017_006",
doi = "10.26615/issn.1314-9156.2017_006",
pages = "41--45",
abstract = {Over the past few years a lot of research has been done on sentiment analysis, however, the emotional analysis, being so subjective, is not a well examined dis-cipline. The main focus of this proposal is to categorize a given sentence in two dimensions - sentiment and arousal. For this purpose two techniques will be com-bined {--} Machine Learning approach and Lexicon-based approach. The first di-mension will give the sentiment value {--} positive versus negative. This will be re-solved by using Na{\"\i}ve Bayes Classifier. The second and more interesting dimen-sion will determine the level of arousal. This will be achieved by evaluation of given a phrase or sentence based on lexi-con with affective ratings for 14 thousand English words.},
}
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%0 Conference Proceedings
%T Gradient Emotional Analysis
%A Simeonova, Lilia
%Y Kovatchev, Venelin
%Y Temnikova, Irina
%Y Gencheva, Pepa
%Y Kiprov, Yasen
%Y Nikolova, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna
%F simeonova-2017-gradient
%X Over the past few years a lot of research has been done on sentiment analysis, however, the emotional analysis, being so subjective, is not a well examined dis-cipline. The main focus of this proposal is to categorize a given sentence in two dimensions - sentiment and arousal. For this purpose two techniques will be com-bined – Machine Learning approach and Lexicon-based approach. The first di-mension will give the sentiment value – positive versus negative. This will be re-solved by using Naïve Bayes Classifier. The second and more interesting dimen-sion will determine the level of arousal. This will be achieved by evaluation of given a phrase or sentence based on lexi-con with affective ratings for 14 thousand English words.
%R 10.26615/issn.1314-9156.2017_006
%U https://doi.org/10.26615/issn.1314-9156.2017_006
%P 41-45
Markdown (Informal)
[Gradient Emotional Analysis](https://doi.org/10.26615/issn.1314-9156.2017_006) (Simeonova, RANLP 2017)
ACL
- Lilia Simeonova. 2017. Gradient Emotional Analysis. In Proceedings of the Student Research Workshop Associated with RANLP 2017, pages 41–45, Varna. INCOMA Ltd..