@thesis{thesis,
		author={Desi  Natalia Br. Sidauruk},
		title ={Analisis Sentimen Menggunakan Word2VEC Dan Support Vector Machine (Studi Kasus: Sentimen Pelanggan Sicepat)},
		year={2021},
		url={https://repository.ittelkom-pwt.ac.id/6697/},
		abstract={E-commerce in Indonesia is growing due to the explosion in the number of
users, that's why freight forwarding services are needed, one of which is Sicepat.
Sicepat is very concerned with customer satisfaction, Sicepat customers can provide
positive, negative, and neutral comments about their services through Twitter. The
opinion of the community, a study was made on sentiment analysis. The sentiment
analysis system is divided into 5 (five) stages, namely data crawling, labeling, preprocessing, feature extraction, and sentiment classification. The method used is
Word2vec as feature extraction by converting data into vector values. The labeling
results are 200 negative classes, 695 positive classes, and 50 neutral classes with
manual labeling while the classification method in this study uses the Support
Vector Machine (SVM). Furthermore, the confusion matrix test gives an accuracy
value of 78%.
Keywords: Sentiment Analysis, Sicepat, Word2vec, Support Vector Machine,
Twitter}
		}