Tweets Emotion Prediction by Using Fuzzy Logic System

The social media world is growing day by day; people are using social media platforms to express their feelings. Leading companies are deploying those platforms in measuring customer's satisfaction with their products and services. The huge amount of data produced by such platforms can be analyzed to help those companies in improving their businesses. This paper introduces a new contribution in the field of emotion classification for Tweets by using Fuzzy Logic intellection that classify each tweet to an emotion with different degrees of intensity. We achieved that by developing two fuzzy classification systems, the first one inspects the text and referred to as (TCFL), while the second one inspects emojis associated with tweet's text and referred to as (ECFL). Our approaches classify tweets into eight different emotion categories (Joy, Sadness, Anger, Disgust, Trust, Fear, Surprise, and Anticipation) with seven emotion degrees (Extremely High, Very High, High, Medium, Low, Very Low, and Extremely Low). After comparing the developed two systems with human-based classification, TCFL outperformed ECFL with 48.96% match as compared to 32.54% match for ECFL.

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