Evaluation of Machine Learning Approach for Sentiment Analysis using Yelp Dataset
Mujiono Sadikin, Abi Fauzan
Abstract
Due to the abundance of text data representing public opinion, the Sentiment Analysis study is getting more and more important. Various techniques and methods have been proposed to address the issues. One of those techniques is deep learning algorithms which have been used to achieve great results in Natural Language Processing (NLP) applications. Sentiment Analysis is a part of NLP application that extracts emotional information from texts. In this study, we investigate the performance of sequence-based model, i.e., LSTM, compared with multi-layer perceptron Neural Network (NN) to classify the polarity of the text review based on negative or positive. The dataset used in this study is a restaurant review taken from the Yelp website. The dataset is trained using Word2vec word embedding to convert words contained in the dataset into numerical vector representation which is used as the deep learning model input. Based on the experiment results, it is shown that the LSTM model is outperformed compared to the multi-layer NN model. The best accuracy performance provided by LSTM model is 91%, whereas the best accuracy performance of multi-layer NN model is 76%.