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Sentiment Analysis on Online Transportation Reviews Using Word2Vec Text Embedding Model Feature Extraction and Support Vector Machine (SVM) Algorithm

In the era of society 5.0, information technology is growing rapidly, one of which is in the field of transportation. The phenomenon of online transportation services is becoming increasingly popular among the public. With this phenomenon, many people have an opinion about online transportation services, both positive and negative comments. The purpose of this study is to conduct sentiment analysis on online transportation service applications, namely reviews of users of the Gojek and Grab applications on the Google Play Store. This research uses the word2vec text embedding model and the support vector machine (SVM) algorithm. Word2vec is used as a feature extraction model as a representation of words into vector form. The architecture of the word2vec model used is the skip-gram model. The Support Vector Machine (SVM) algorithm is used for the data classification process to determine the level of accuracy of the data sentiment used. The results of the tests carried out on the classification of sentiment analysis on online transportation applications show that the performance results are quite good namely, the Gojek application gets a higher performance value with an accuracy value of 89%, precision of 94%, re-call of 86% and f1-score of 90%. Meanwhile, the Grab application has an accuracy value of 87%, a precision of 94%, a re-call of 85%, and an f1-score of 89%.