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Article summary:
1. The bullet subtitle has its own unique characteristics, making existing sentiment classification methods not ideal for the task.
2. A Chinese Bullet Subtitle Sentiment Lexicon is proposed to address this issue.
3. An affective computing and ensemble learning-based sentiment classification method is proposed to improve accuracy and practical application effect.
Article analysis:
The article provides a comprehensive overview of the current state of sentiment classification for bullet subtitles, as well as a proposed solution to improve accuracy and practical application effect. The article is well-structured and provides evidence for the claims made, such as the use of existing sentiment lexicons and affective computing methods. However, there are some potential biases in the article that should be noted. For example, the article does not explore any counterarguments or present both sides equally when discussing existing sentiment classification methods or proposing a new method. Additionally, there is no discussion of possible risks associated with using the proposed method or any other potential drawbacks that should be considered before implementation. Furthermore, while the article does provide evidence for its claims, it does not provide any external sources or references to back up these claims which could further strengthen its trustworthiness and reliability.