Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory

Abstract

Advances in information technology have given rise to online hotel reservation options. The user review feature is an important factor during the online booking of hotels. Generally, most online hotel booking service providers provide review and rating features for assessing hotels. However, not all service providers provide rating features or recap reviews for every aspect of the hotel services offered. Therefore, we propose a method to summarise reviews based on multiple aspects, including food, room, service, and location. This method uses long short-term memory (LSTM), together with hidden layers and automation of the optimal number of hidden neurons. The F1-measure value of 75.28% for the best model was based on the fact that (i) the size of the first hidden layer is 1,200 neurons with the tanh activation function, and (ii) the size of the second hidden layer is 600 neurons with the ReLU activation function. The proposed model outperforms the baseline model (also known as standard LSTM) by 10.16%. It is anticipated that the model developed through this study can be accessed by users of online hotel booking services to acquire a review recap on more specific aspects of services offered by hotels

Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews

“…In particular, fully connected layer 1 yielded optimal parameters for 1200 neurons with the tanh activation function, and fully connected layer 2 yielded optimal parameters for 600 neurons with the ReLU activation function. Compared with the standard LSTM model, our proposed model (modified LSTM with two fully connected layers) performs better at 10.16% [ 43 ].…”

Determining community happiness index with transformers and attention-based deep learning

“…The survey conducted by Cendani et al [7] added applying the attention mechanism layer after the LSTM layer to improve the model's performance. In contrast, the study conducted by Jayanto et al [6] did not use the attention mechanism layer. However, using Word2Vec as a word embedding technique has problems overcoming the context of words in a sentence.…”

“…The approach uses the long short-term memory (LSTM) model with word-to-vector (Word2Vec) as the word embedding technique. It obtained an f1-score value of 75.28% for the best model based on the first hidden layer size of 1,200 neurons with tanh activation function and the second discreet layer size of 600 neurons with rectified linear unit (ReLU) activation function [6].…”

“…In terms of previous studies, the study conducted by Jayanto et al [6] and Cendani et al [7] both use the Word2Vec model. The survey conducted by Cendani et al [7] added applying the attention mechanism layer after the LSTM layer to improve the model's performance.…”

Labeling Consistency Test of Multi-Label Data for Aspect and Sentiment Classification Using the Cohen Kappa Method

“…Preprocessing is used to clean the dataset. The preprocessing process uses stages (case folding, stop-word removal, stemming, tokenization, padding, and vectorization) [34]. Next, we extract the data to the graph using the stanza library dependency graph (http://stanza.run/(accessed on 9 March 2023).…”