TY - GEN
T1 - Emotion recognition in a conversational context
AU - Chakraborty, Binayaka
AU - Geetha, M.
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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U2 - 10.1007/978-981-13-2907-4_18
DO - 10.1007/978-981-13-2907-4_18
M3 - Conference contribution
AN - SCOPUS:85072872537
SN - 9789811329067
T3 - Communications in Computer and Information Science
SP - 208
EP - 214
BT - Applications and Techniques in Information Security - 9th International Conference, ATIS 2018, Proceedings
A2 - Chen, Qingfeng
A2 - Wu, Jia
A2 - Zhang, Shichao
A2 - Yuan, Changan
A2 - Batten, Lynn
A2 - Li, Gang
PB - Springer Verlag
T2 - 9th International Conference on Applications and Techniques in Information Security, ATIS 2018
Y2 - 9 November 2018 through 11 November 2018
ER -