Text Preprocessing on Emotional Tweets Case Study: Covid-19 Vaccine Rejection Text Preprocessing pada Tweet Emosi Studi Kasus : Penolakan Vaksin Covid-19

Abstract

Emotion, both verbal and non-verbal, plays a crucial role in expressing opinions, especially in the digital realm of text-based social media. Emotion classification becomes essential for extracting and categorizing responses or opinions expressed by individuals on various issues or events. This study focuses on classifying emotions conveyed in text-based opinions extracted from Twitter. Utilizing a dictionary-based approach, the research aims to classify emotions into seven categories: anticipation, pleasure, trust, anger, disgust, fear, and sadness. Through practical work, the author develops a dictionary comprising key words for each emotion aspect, facilitating accurate classification. The results contribute to enhancing understanding and analyzing emotional expressions in online discourse, offering valuable insights for sentiment analysis applications and social media monitoring tools.

Highlights :

• Emotion classification crucial for understanding online opinions.


• Study focuses on categorizing emotions from Twitter text.


• Dictionary-based approach enhances accuracy in emotion classification.

Keywords : Emotion classification, text-based social media, Twitter, dictionary-based approach, sentiment analysis.