Classifying Text-Based Emotions Using Logistic Regression

Fahad Mazaed Alotaibi


Emotion detection textual content is getting popular among individuals and business companies to analyze user emotional reaction on the products they use. In this work, emotion detection from textual content is performed by using supervised learning-based Logistic Regression classifier. ISEAR dataset is used to taring the classifier, while testing dataset is used to evaluate the prediction capability of the classifier for emotion classification. The prior works used rule-based techniques, supported by lexical resources. However, limited coverage of emotional clues, was the major issue, which resulted in poor performance of system. The proposed work overcomes this limitation by proposing supervised learning technique using Logistic Regression classifier. The results obtained are encouraging and show that the proposed system performed better than the similar methods.

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