Emotion Recognition in a Conversational Context

Chakraborty, Binayaka and Geetha, M (2018) Emotion Recognition in a Conversational Context. In: International Conference on Applications and Techniques in Information Security”, ATIS 2018, 09/11/2018, Nanning, China.

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Abstract

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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Machine learning � Deep learning � RAKEL � Naïve Bayes Sentiment analysis � Multi-labelled emotions
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Depositing User: MIT Library
Date Deposited: 09 Jan 2019 09:13
Last Modified: 09 Jan 2019 09:13
URI: http://eprints.manipal.edu/id/eprint/152783

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