Text-Based Emotion Sentiment Analysis on Social Media Using NLP and Lexicon Approach (Case Study: Gaza Conflict)

Athaya Zakira, Muhammad Arhami, Musta’inul Abdi, Safriadi Safriadi

Abstract


Social media is an internet platform that allows individuals to virtually interact, share and form social bonds. Expressing emotions through images, videos, and texts is becoming a trend, but the uncertainty in understanding the type of emotions from texts is a challenge. Sentiment analysis can help identify emotions in social media texts, especially through the use of the Naive Bayes method. In this study, the data to be used is sourced from the social media platform Twitter and the classification of emotions in the categories specified in the , namely positive, negative, and neutral emotions. By applying Naive Bayes and NLP (Natural Languange Processing), it can overcome language variations and automatically analyze text, so as to identify emotional expressions in complex formats, such as emojis or unconventional expressions. This research can produce a classification model that can help identify and understand the emotions contained in social media texts more efficiently. The classification results for sentiment analysis of the conflict in Gaza received a percentage of 64.4%, with a  of 80%, recall of 50%, and f1-score of 48%.

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