Shoe Review Sentiment Analysis Using Machine Learning and Deep Learning with Word2vec
In this modern era, internet use is continually increasing and will run with an increasing amount of existing data, such as text data. Characteristics of unstructured text are a challenge in text processing feature extraction and encourage sentiment analysis research to be carried out. The availability of a lot of text data on the internet is a challenge in sentiment analysis because it requires a complex approach. This study uses the baseline Deep Learning (DL) method, namely Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) with word2vec. It uses Machine Learning (ML), namely Random Forest (RF), Support Vector Machine (SVM), and Gaussian Naïve Bayes (GNB) with the proposed method, word2vec, and the dataset used is the shoe review dataset which consists of 389,877 reviews. From the discussion above, to carry out sentiment analysis, a more suitable method is to use the CNN baseline method with word embedding word2vec to get an accuracy value of 91.53%. The novelty of this study is the increase in the classification accuracy value from previous studies.