COMPARISON OF SUPPORT VECTOR MACHINE BASED ON FASTTEXT WITHOUT AND WITH FIREFLY OPTIMIZATION PARAMETERS FOR DISASTER SENTIMENT ANALYSIS IN INDONESIA - Adhel, Fadilah Amirul; Thamrin, Sri Astuti; Siswanto, Siswanto (PATTIMURA UNIVERSITY, 2024)
Sentiment analysis is a process for analyzing opinions, sentiments, assessments, and emotions from someone's statements regarding a domain or is also a process for entering and processing data in the form of text. Support vector machine (SVM) is a supervised machine learning technique that functions as a separator of two classes of data. SVM aims to obtain numerical vectors using fasttext. SVM cannot choose appropriate parameters so the use of parameters is not optimal. To obtain optimal parameters with better classification results, firefly optimization was carried out. This research compares the fasttext-based SVM method without and with firefly optimization parameters using data from tweets with the keyword