Advertisement

Contextual Emotion Detection in Text Using Ensemble Learning

  • S. Angel DeborahEmail author
  • S. RajalakshmiEmail author
  • S. Milton RajendramEmail author
  • T. T. MirnalineeEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

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.

Keywords

Sentiment analysis Emotion detection Machine learning techniques Ensemble methods Text mining 

References

  1. 1.
    Chuang, Z.J., Wu, C.H.: Emotion recognition from textual input using an emotional semantic network. In: ICSLP (2002)Google Scholar
  2. 2.
    Mubasher, H., Raza, S.A., Shehzad, H.M.: Context based emotion analyzer for interactive agent. Int. J. Adv. Comput. Sci. Appl. (2017)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Tao, J., Tan, T.: Emotional Chinese talking head system. In: Proceedings of the 6th International Conference on Multimodal Interfaces (2004)Google Scholar
  10. 10.
    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)CrossRefGoogle Scholar
  11. 11.
    Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge (2012)CrossRefGoogle Scholar
  12. 12.
    Natekin, A., Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobot. 7, 1–21 (2013)CrossRefGoogle Scholar
  13. 13.
    He, B., Guan, Y., Cheng, J., Cen, K., Hua, W.: CRFs based de-identification of medical records. J. Biomed. Inform. 58, S39–S46 (2015)CrossRefGoogle Scholar
  14. 14.
    Friedman, J.: Greedy boosting approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSSN College of EngineeringChennaiIndia

Personalised recommendations