| dc.contributor | 資科系 | |
| dc.creator (作者) | 黃瀚萱* | |
| dc.creator (作者) | Huang, Hen-Hsen | |
| dc.creator (作者) | Wang, Jun‐Jie | |
| dc.creator (作者) | Chen*, Hsin-Hsi | |
| dc.date (日期) | 2017-09 | |
| dc.date.accessioned | 5-Mar-2020 14:40:44 (UTC+8) | - |
| dc.date.available | 5-Mar-2020 14:40:44 (UTC+8) | - |
| dc.date.issued (上傳時間) | 5-Mar-2020 14:40:44 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/129118 | - |
| dc.description.abstract (摘要) | Opinion words are crucial information for sentiment analysis. In some text, however, opinion words are absent or highly ambiguous. The resulting implicit opinions are more difficult to extract and label than explicit ones. In this paper, cutting-edge machine-learning approaches – deep neural network and word-embedding – are adopted for implicit opinion mining at the snippet and clause levels. Hotel reviews written in Chinese are collected and annotated as the experimental data set. Results show the convolutional neural network models not only outperform traditional support vector machine models, but also capture hidden knowledge within the raw text. The strength of word-embedding is also analyzed. | |
| dc.format.extent | 498543 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Journal of the Association for Information Science and Technology, Vol.68, pp.2076-2087 | |
| dc.title (題名) | Implicit Opinion Analysis: Extraction and Polarity Labelling | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1002/asi.23835 | |
| dc.doi.uri (DOI) | https://doi.org/10.1002/asi.23835 | |
