Abstract
As human beings, it is hard to interpret the presence of emotions such as sadness or disgust in a sentence without the context, and the same ambiguity exists for machines also. Emotion detection from facial expressions and voice modulation easier than emotion detection from text. Contextual emotion detection from text is a challenging problem in text mining. Contextual emotion detection is gaining importance, as people these days are communicating mainly through text messages, to provide emotionally aware responses to the users. This work demonstrates ensemble learning to detect emotions present in a sentence. Ensemble models like Random Forest, Adaboost and Gradient Boosting have been used to detect emotions. Out of the three models, it has been found that Gradient Boosting Classifiers predicts the emotions better than the other two classifiers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chuang, Z.J., Wu, C.H.: Emotion recognition from textual input using an emotional semantic network. In: ICSLP (2002)
Mubasher, H., Raza, S.A., Shehzad, H.M.: Context based emotion analyzer for interactive agent. Int. J. Adv. Comput. Sci. Appl. (2017)
Kar, S., Maharjan, S., Solorio, T.: RiTUAL-UH at SemEval-2017 task 5: sentiment analysis on financial data using neural networks. In: Proceedings of the 11th International Workshop on Semantic Evaluation, pp. 877–882 (2017)
Rajalakshmi, S., Angel Deborah, S., Milton Rajendram, S., Mirnalinee, T.T.: SSN MLRG1 at SemEval-2018 Task 3: irony detection in English tweets using multilayer perceptron. In: Proceedings of the 12th International Workshop on Semantic Evaluation, pp. 633–637 (2018)
Angel Deborah, S., Rajalakshmi, S., Milton Rajendram, S., Mirnalinee, T.T.: SSN MLRG1 at SemEval-2018 Task 1: emotion and sentiment intensity detection using rule based feature selection. In: Proceedings of the 12th International Workshop on Semantic Evaluation, pp. 324–328 (2018)
Angel Deborah, S., Milton Rajendram, S., Mirnalinee, T.T.: SSN_MLRG1 at SemEval-2017 Task 5: fine-grained sentiment analysis using multiple kernel gaussian process regression model. In: Proceedings of the 11th International Workshop on Semantic Evaluation, pp. 823–826 (2017)
Angel Deborah, S., Milton Rajendram, S., Mirnalinee, T.T.: SSN_MLRG1 at SemEval-2017 Task 4: sentiment analysis in twitter using multi-kernel gaussian process classifier. In: Proceedings of the 11th International Workshop on Semantic Evaluation, pp. 709–712 (2017)
Pivovarova, L., Escoter, L., Klami, A., Yangarber, R.: HCS at SemEval-2017 Task 5: sentiment detection in business news using convolutional neural networks. In: Proceedings of the 11th International Workshop on Semantic Evaluation, pp. 842–846 (2017)
Tao, J., Tan, T.: Emotional Chinese talking head system. In: Proceedings of the 6th International Conference on Multimodal Interfaces (2004)
Gaber, T., Tharwat, A., Hassanien, A.E., Snasel, V.: Biometric cattle identification approach based on Weber’s Local Descriptor and AdaBoost classifier. Comput. Electron. Agric. 122, 55–66 (2016)
Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge (2012)
Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 1–21 (2013)
He, B., Guan, Y., Cheng, J., Cen, K., Hua, W.: CRFs based de-identification of medical records. J. Biomed. Inform. 58, S39–S46 (2015)
Friedman, J.: Greedy boosting approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Angel Deborah, S., Rajalakshmi, S., Milton Rajendram, S., Mirnalinee, T.T. (2020). Contextual Emotion Detection in Text Using Ensemble Learning. In: Hemanth, D.J., Kumar, V.D.A., Malathi, S., Castillo, O., Patrut, B. (eds) Emerging Trends in Computing and Expert Technology. COMET 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-030-32150-5_121
Download citation
DOI: https://doi.org/10.1007/978-3-030-32150-5_121
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32149-9
Online ISBN: 978-3-030-32150-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)