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Human-to-human communication can be achieved not only by body language but also by high-level language. Moreover, information can be conveyed in writing. In particular, the high-level and specific process of logical thinking can be expressed in writing. Text is data that we encounter daily, and there are hidden patterns in it. A person’s cognitive activity, that is, text data, contains the author’s emotions. In the existing text analysis method, simply using the frequency of words has limited interpretability. The model proposed in this paper is a nonlinear emotion system based on emotion to increase document diversity. The purpose is to effectively converge features by assigning weights to a nonlinear function with existing training and learning methods. Our study used the confusion matrix, an area under the receiver operating characteristic curve, and F1-score as evaluation methods. This research created a new error function and measured emotions. The accuracy was 0.9447, and the model’s receiver operating curve peak was 0.9845, which is somewhat similar to that of TF-IDF in the evaluation.

Effective Emotion Recognition Technique in NLP Task over Nonlinear Big Data Cluster