期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2025-12-04 |
| 作品名稱 | Hybrid Hierarchical Attention Network-Hierarchical Deep Learning for Text Classification in Opinion Mining |
| 作品名稱（其他語言） | |
| 著者 | Tzu-Chia Chen |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Concurrency and Computation: practice and experience 38(1) ,p. e70445 |
| 摘要 | In general, opinion mining indicates the process of evaluating the opinions of people on several topics that are accessible in text form. It is an important aspect of natural language processing as it sets up the effective planning and decision-making for businesses and users. Opinion mining can be performed more effectively and conveniently by initially carrying out subjectivity recognition, which entails recognizing the text as objective or subjective. This research comprises various steps, like preprocessing, feature extraction, data augmentation and opinion mining. The complete procedure was implemented in the Spark framework that utilizes a master–slave framework. The preprocessing step is done with methods, such as stop-word removal, stemming, and lemmatization. Afterwards, feature extraction is done by extracting sentiWordNet features and statistical features that involve capitalized words, exclamation marks, and hashtags. Followed by the data augmentation, the opinion mining phase uses a HAN–HDLTex approach proposed by the combination of HAN and HDLTex architectures. The experimentation is done for the proposed HAN–HDLTex model that shows better accuracy with a rate of 0.949, sensitivity with a rate of 0.969, and specificity with a rate of 0.939. |
| 關鍵字 | |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128284 ) |