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
- 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
- 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)
- PDF:
- https://doi.org/10.26615/issn.1314-9156.2017_006