The Implementation of TF-IDF and Word2Vec on Booster Vaccine Sentiment Analysis Using Support Vector Machine Algorithm

As a sort of technological advancement, social media is a medium used to transmit ideas on certain subjects. Sentiment analysis can be used to analyze public opinion. Feature extraction stage of sentiment analysis is crucial for transforming unstructured text into categorizable structured data. Usin...

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Bibliographic Details
Published in:Procedia computer science Vol. 234; pp. 156 - 163
Main Authors: Nurhaliza Agustina, C.A, Novita, Rice, Mustakim, Rozanda, Nesdi Evrilyan
Format: Journal Article
Language:English
Published: Elsevier B.V 2024
Subjects:
Feature Extraction
Sentiment Analysis
Support Vector Machine
TF-IDF
Word2Vec
Feature Extraction
Sentiment Analysis
Support Vector Machine
TF-IDF
Word2Vec
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary:As a sort of technological advancement, social media is a medium used to transmit ideas on certain subjects. Sentiment analysis can be used to analyze public opinion. Feature extraction stage of sentiment analysis is crucial for transforming unstructured text into categorizable structured data. Using 13,297 records from Twitter and SVM algorithm, as well as the TF-IDF and Word2Vec feature extraction approaches, the combination of SVM + TF-IDF with 80:20 data split scenario and the RBF kernel produces the best results, with precision 85%, recall 86%, and f1-score 84%. In the 80:20 data split and RBF kernel, SVM+Word2Vec combination achieves the highest performance, with precision 83%, recall 82%, and f1-score 76%.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2024.02.162