Solving the Problem of Classifying the Emotional Tone of a Message with Determining the Most Appropriate Neural Network Architecture
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Abstract
To determine the most effective approach for solving the task of classifying the emotional tone of a message, we trained selected neural network models on various sets of training data. Next, based on the performance metric of the percentage of correctly classified responses on a test data set, we compared combinations of training data sets and various models trained on them. During the writing of this article, we trained four neural network models on three different sets of training data. By comparing the accuracy of the responses from each model trained on different training data sets, conclusions were drawn regarding the neural network model best suited for solving the task at hand.
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References
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