Authors :
Ajay Sathish Preetha
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/s3cbddah
Scribd :
https://tinyurl.com/36c5r4b7
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR640
Abstract :
Recent methods of AI have revolutionized the
field of computer science. Different sub-sectors of
artificial intelligence (AI), like natural language
processing (NLP) models, generative AI, computer vision,
autonomous and recommendation systems, cybersecurity,
quantum computing, etc., have helped automate human
tasks, resulting in a tremendous amount of time and
energy being saved. Despite the massive development of
AI, all AI models lack one major factor, which is emotion.
How can emotion be built into AI in order to for it to
develop the emotional intelligence of the human brain to
interpret and understand emotions so that it could create
more human-friendly interactions? In this paper, we
hypothesized developing emotions in neural networks as
predictive sentiment analysis models using text data in
order to replicate the emotional intelligence of the human
brain to benefit human relationships. By using the
Anaconda Repository, NVIDIA’s CUDA Toolkit, and
TensorFlow, we were able to create a sentiment
prediction model that achieved an accuracy of 94% and
predicted the six basic emotions of joy, sadness, anger,
fear, love, and surprise. Concluding this research, we
observed that neural networks can develop the habit of
recognizing emotions. This can be further fed into
complex AI algorithms and systems to fine-tune
emotional intelligence, resulting in more natural
interactions, benefiting humans in
Keywords :
Artificial Intelligence, Emotion in AI, Neural Networks, Sentiment Analysis, Emotional Intelligence, Human-friendly Interactions, Natural Language Processing (NLP), Generative AI, Computer Vision, Autonomous Systems, Recommendation Systems, Cybersecurity, Quantum Computing, Anaconda Repository, NVIDIA’s CUDA Toolkit.
Recent methods of AI have revolutionized the
field of computer science. Different sub-sectors of
artificial intelligence (AI), like natural language
processing (NLP) models, generative AI, computer vision,
autonomous and recommendation systems, cybersecurity,
quantum computing, etc., have helped automate human
tasks, resulting in a tremendous amount of time and
energy being saved. Despite the massive development of
AI, all AI models lack one major factor, which is emotion.
How can emotion be built into AI in order to for it to
develop the emotional intelligence of the human brain to
interpret and understand emotions so that it could create
more human-friendly interactions? In this paper, we
hypothesized developing emotions in neural networks as
predictive sentiment analysis models using text data in
order to replicate the emotional intelligence of the human
brain to benefit human relationships. By using the
Anaconda Repository, NVIDIA’s CUDA Toolkit, and
TensorFlow, we were able to create a sentiment
prediction model that achieved an accuracy of 94% and
predicted the six basic emotions of joy, sadness, anger,
fear, love, and surprise. Concluding this research, we
observed that neural networks can develop the habit of
recognizing emotions. This can be further fed into
complex AI algorithms and systems to fine-tune
emotional intelligence, resulting in more natural
interactions, benefiting humans in
Keywords :
Artificial Intelligence, Emotion in AI, Neural Networks, Sentiment Analysis, Emotional Intelligence, Human-friendly Interactions, Natural Language Processing (NLP), Generative AI, Computer Vision, Autonomous Systems, Recommendation Systems, Cybersecurity, Quantum Computing, Anaconda Repository, NVIDIA’s CUDA Toolkit.
