Performance Comparison of SVM in Sentiment Analysis of Israel-Palestine Comments Using Lsa and Word2vec

This study compares two feature extraction techniques, namely Latent Semantic Analysis (LSA) and Word2Vec, in the sentiment classification of comments related to the Israeli-Palestinian conflict using Support Vector Machine (SVM). The dataset consists of 1000 YouTube comments and 158 news paragraphs, categorized into pro and con Palestinian sentiments. The preprocessing process includes casefolding, special character and stopword removal, lemmatization, and tokenization. The results show that SVM with Word2Vec has better performance than SVM with LSA in the classification of positive and negative comments. SVM model with Word2Vec recorded a precision value of 92% and F1-Score of 93% on negative comments. Meanwhile, SVM with LSA recorded 90% precision and 92% F1-Score. On positive comments, SVM with Word2Vec recorded 92% recall and 93% F1-Score. While SVM with LSA recorded 89% recall and 91% F1-Score. Word2Vec's strength lies in its ability to capture word context and nuance more effectively, thanks to training using richer contextualized comment and news data. In conclusion, although both methods show good ability in sentiment classification, the use of Word2Vec provides more consistent and accurate results. This research contributes to the advancement of sentiment classification methods in the context of complex socio-political issues and can serve as a reference for applying machine learning to more accurate and contextual public opinion analysis.