Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features
| dc.contributor.author | Pasupa, Kitsuchart | |
| dc.contributor.author | Seneewong Na Ayutthaya, Thititorn | |
| dc.date.accessioned | 2026-08-06T10:25:58Z | |
| dc.date.available | 2026-08-06T10:25:58Z | |
| dc.date.issued | 2019-10-01 | |
| dc.description.abstract | A smart city connects physical, information technology, social, and business infrastructures together to leverage their collective intelligence. Feedback drives improvements in service, city development, and quality of life in the city. Therefore, sentiment analysis in real-time of opinions expressed in text form by residents in the city is absolutely necessary. Nowadays, machine learning is widely applied to sentiment analysis of decisions in business, especially deep learning. In this experiment, we evaluated and compared the performances of several conventional deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM), in sentiment analysis of Thai children tales. In several previous studies, many features have been used in all of the models mentioned, features such as word embedding that helps a model to understand the semantics of each word, POS-tag that helps a model to understand the grammatical function of words, and sentic that helps a model to understand the emotion of words. Some combinations of these features have also been used. The results of this experiment show that the CNN model that used all three features gave the best result of 0.817 F1-score at p < 0.01, which was significantly better than all other models. | |
| dc.identifier.citation | Sustainable Cities and Society, 50, 2019 | |
| dc.identifier.doi | 10.1016/j.scs.2019.101615 | |
| dc.identifier.issn | 22106707 | |
| dc.identifier.other | 2-s2.0-85067187685 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/10251 | |
| dc.source | Sustainable Cities and Society | |
| dc.subject | Deep learning | |
| dc.subject | Part-of-speech | |
| dc.subject | SenticNet | |
| dc.subject | Sentiment analysis | |
| dc.subject | Thai sentiment | |
| dc.subject | Word embedding | |
| dc.title | Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features | |
| dc.type | Article |