Gradient Emotional Analysis

Lilia Simeonova


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 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.
Anthology ID:
R17-2006
Volume:
Proceedings of the Student Research Workshop Associated with RANLP 2017
Month:
September
Year:
2017
Address:
Varna
Editors:
Venelin Kovatchev, Irina Temnikova, Pepa Gencheva, Yasen Kiprov, Ivelina Nikolova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
41–45
Language:
URL:
https://doi.org/10.26615/issn.1314-9156.2017_006
DOI:
10.26615/issn.1314-9156.2017_006
Bibkey:
Cite (ACL):
Lilia Simeonova. 2017. Gradient Emotional Analysis. In Proceedings of the Student Research Workshop Associated with RANLP 2017, pages 41–45, Varna. INCOMA Ltd..
Cite (Informal):
Gradient Emotional Analysis (Simeonova, RANLP 2017)
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PDF:
https://doi.org/10.26615/issn.1314-9156.2017_006