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Sentiment Analysis With Fully Supervised Speaker Diarization
Speaker diarization with emotions analysis has been one of the most difficult tasks of the Machine Learning and Artificial Intelligence genre. But, this problem is now resolved by some well-known tech companies like Google but it depends upon whether they have open-sourced their work and not, it made a specific population aware of it. In this paper, RNN approach would be discussed in order to provide better accuracy for the sequential data and its advantages over the clustering process, why it best to work with RNN if your data has labels, Using Deep Affects API We will give the actual visualization of the whole conversation between the people, what were their emotions during their phrases what was their overall sentiment of the conversation involving plenty of emotions in the trained data it gives better accuracy and even shows it on the visualization page.