閱讀全文 | |
篇名 |
Chinese Microblog Sentiment Analysis by Adding Emoticons to Attention-Based CNN
|
---|---|
並列篇名 | Chinese Microblog Sentiment Analysis by Adding Emoticons to Attention-Based CNN |
作者 | Yi-Jen Su、Chao-Ho Chen、Tsong-Yi Chen、Cheng-Chan Cheng |
英文摘要 | Nowadays, people are used to sharing their views and ideas on social media platforms, which generates enormous amounts of data every day. This research adopted sentiment analysis to disclose embedded information in Chinese short texts, which can serve as an integral part of social media monitoring and analytics. The research proposed a deep learning method, Attention-of-Emoticons Based Convolutional Neural Network (AEB-CNN), by integrating emoticons and attention-based mechanisms with CNN to enhance the accuracy of sentiment analysis. An implementation was carried out by TensorFlow; the accuracy of sentiment polarity of Chinese microblogs reached somewhere between 85.1% and 89.1% while achieving shorter execution time compared to other methods when the size of training dataset ranged from 10,000 to 30,000 sentences. |
起訖頁 | 821-829 |
關鍵詞 | Sentiment analysis、Attention-based、CNN、Emoticon |
刊名 | 網際網路技術學刊 |
期數 | 202005 (21:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
|
QR Code | |
該期刊 上一篇
| A Dual-branch CNN Structure for Deformable Object Detection |
該期刊 下一篇
| Attention-based Recurrent Neural Network for Traffic Flow Prediction |