Abstract
Nowadays, millions of users use many social media systems every day. These services produce massive messages, which play a vital role in the social networking paradigm. As we see, an intelligent learning emotion system is desperately needed for detecting emotion among these messages. This system could be suitable in understanding users’ feelings towards particular discussion. This paper proposes a text-based emotion recognition approach that uses personal text data to recognize user’s current emotion. The proposed approach applies Dominant Meaning Technique to recognize user’s emotion. The paper reports promising experiential results on the tested dataset based on the proposed algorithm.
Highlights
In collaborative chatting between users, emotions are an important aspect
This paper presents a new technique based on Dominant Meaning Technique [6] and Appraisal Method [7] to classify a text to a suitable emotion
Text-Based Emotion detection becomes an important research field with the massive chatting messages coming from social media systems
Summary
In collaborative chatting between users, emotions are an important aspect. The detection of the exchange of emotions among users through text messages can help for delivering right emotion in the right time. Several researches used textbased emotion to predict and classify the emotion types, such as [1] [2] [3] and [4]. Jraidi et al [5] show the impact of using emotion in intelligent system and show how these emotions oriented toward developing emotionally sensitive tutors. This paper presents a new technique based on Dominant Meaning Technique [6] and Appraisal Method [7] to classify a text to a suitable emotion.
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