期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2019
卷号：11
页码：227-241
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：Recently, online buyers have been able to express
their opinions about a variety of products, restaurants and
services by writing online reviews. Opinions have subsequently
become a new, important, and influential source of information
for decision-making. This paper implements binary and
multiclass sentiment classifications on a dataset of online reviews.
The experiments are performed using restaurant reviews from
Yelp to predict ratings based on the content of the reviews. This
paper investigates various structures of neural networks on
restaurant reviews, such as recurrent neural networks (RNNs)
with long short-term memory (LSTM), RNNs with bidirectional
LSTM (Bi-LSTM) and convolutional neural networks (CNNs).
The reviews were first converted into vectors during
preprocessing using various features: pretrained word2vec and
global vector (GloVe) embedding. The efficacy of these text
classification techniques was examined and compared. The
classification performance was evaluated using different metrics:
the accuracy, confusion matrix, recall, specificity, precision, F1
score, receiver-operating characteristic (ROC) curve, and the
area under the curve (AUC). The results showed that the RNN
model achieved a better accuracy score with Bi-LSTM for both
binary and multiple sentiment classification tasks.
关键词：Sentiment analysis; Text mining; Star Rating; LSTM;
CNN; RNN
