Abstract
Abstract: Sentiment classification is a crucial problem in natural language processing and is essential to understand user opinions. There are two main approaches to solve this problem, one is the classified-based method, the other is the lexicon-based method; however, both methods perform not well on the long-sequence methods, and each method has its advantages and disadvantages. This paper introduced a new method called Lexiconed BERT, which cream off the best and filter out the impurities from the above two methods. The evaluation shows that our model achieves excellent results in the long sequence sentence and reduce resource consumption significantly.
Original language | English |
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Article number | 032113 |
Number of pages | 7 |
Journal | Journal of Physics: Conference Series |
Volume | 1802 |
Issue number | 3 |
Early online date | 15 Nov 2020 |
DOIs | |
Publication status | Published - 9 Mar 2021 |
Event | The 7th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation - Busan, Korea, Democratic People's Republic of Duration: 14 Nov 2020 → 15 Nov 2020 http://www.iccdmms.org/ |
Keywords
- sentiment analysis
- BERT
- lexicon-based method