Emotion Analysis in Text using TF-IDF

A myriad of the population has adapted to the evolving technology, which includes text communication. Users advertently or inadvertently share emotions. As we know, emotions are one of the most critical aspects of human life; they impact human's behavior, thinking, compelling of action, and most important, decision making. There are many alleged emotions known to us, and each having its significance. In this era of modern technology, it is hard to find any unexplored area; this applies to emotion. People express their emotions through text a lot nowadays, which has led the Emotion Recognition as an important research area. Extracting emotion is a very complicated task. This paper shows a new approach to detect emotion based on TFIDF, and it is a measure that reflects the value a word holds in a document. In this method, emotion is classified into six types. There are other researches on the simple distinction between positive and negative emotion, but this does not add much to understanding human emotion. Emotion is extracted from different sentences, and data representation is based on semantic structure. It generalizes each sentence into six major predefined emotion sets. The evaluation shows that this method is well accomplished to categorize a sentence into different emotion categories and with a reasonable accuracy rate.