Emotion recognition in a conversational context

Binayaka Chakraborty, M. Geetha

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The recent trends in Artificial Intelligence (AI) are all pointing towards the singularity, i.e., the day the true AI is born, which can pass the Turing Test. However, to achieve singularity, AI needs to understand what makes a human. Emotions define the human consciousness. To properly understand what it means to be human, AI needs to understand emotions. A daunting task, given that emotions may be very different, for different people. All these get even more complex when we see that culture plays a great role in expressions present in a language. This paper is an attempt to classify text into compound emotional categories. The proposal of this paper is identification of compound emotions in a sentence. It takes three different models, using Deep Learning networks, and the more traditional Naïve Bayes model, while keeping the mid-field level using RAKEL. Using supervised analysis, it attempts to give an emotional vector for the given set of sentences. The results are compared, showing the effectiveness of Deep Learning networks over traditional machine learning models in complex cases.

Original languageEnglish
Title of host publicationApplications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings
EditorsQingfeng Chen, Jia Wu, Shichao Zhang, Changan Yuan, Lynn Batten, Gang Li
PublisherSpringer Verlag
Number of pages7
ISBN (Print)9789811329067
Publication statusPublished - 2018
Event9th International Conference on Applications and Techniques in Information Security, ATIS 2018 - Nanning, China
Duration: 09-11-201811-11-2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference9th International Conference on Applications and Techniques in Information Security, ATIS 2018

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)


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