Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Ahmad Dahlan
2022-11-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | https://ijain.org/index.php/IJAIN/article/view/691 |
| _version_ | 1797944828457123840 |
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| author | Rahmat Jayanto Retno Kusumaningrum Adi Wibowo |
| author_facet | Rahmat Jayanto Retno Kusumaningrum Adi Wibowo |
| author_sort | Rahmat Jayanto |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-04-10T20:44:39Z |
| format | Article |
| id | doaj.art-572642d9937149aaa19c19d0d9c8d990 |
| institution | Directory Open Access Journal |
| issn | 2442-6571 2548-3161 |
| language | English |
| last_indexed | 2024-04-10T20:44:39Z |
| publishDate | 2022-11-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj.art-572642d9937149aaa19c19d0d9c8d9902023-01-24T08:52:21ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612022-11-018339140310.26555/ijain.v8i3.691220Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memoryRahmat Jayanto0Retno Kusumaningrum1Adi Wibowo2Department of Informatics, Universitas DiponegoroDepartment of Informatics, Universitas DiponegoroDepartment of Informatics, Universitas DiponegoroAdvances 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 hotelshttps://ijain.org/index.php/IJAIN/article/view/691aspect-based sentiment analysisdeep neural networklong short-term memoryhotel reviewindonesian language |
| spellingShingle | Rahmat Jayanto Retno Kusumaningrum Adi Wibowo Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory IJAIN (International Journal of Advances in Intelligent Informatics) aspect-based sentiment analysis deep neural network long short-term memory hotel review indonesian language |
| title | Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory |
| title_full | Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory |
| title_fullStr | Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory |
| title_full_unstemmed | Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory |
| title_short | Aspect-based sentiment analysis for hotel reviews using an improved model of long short-term memory |
| title_sort | aspect based sentiment analysis for hotel reviews using an improved model of long short term memory |
| topic | aspect-based sentiment analysis deep neural network long short-term memory hotel review indonesian language |
| url | https://ijain.org/index.php/IJAIN/article/view/691 |
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