融合情感分析与强化学习，提升新闻推荐的个性化与适应性。
Improving News Recommendations through Hybrid Sentiment Modelling and Reinforcement Learning
- 用多种情感工具融合打分，生成更可靠的新闻情绪标签。
- 在BBC新闻数据集上，推荐准确率提升12.3%。
- 适合关注情绪感知推荐系统的研究者与产品设计者。
新闻推荐系统依赖自动化情感分析来实现内容个性化并提升用户参与度。传统方法常因语义模糊、词典不一致和上下文理解不足而受限，尤其在多源新闻环境中表现不佳。现有模型通常将情感视为次要特征，难以适应用户的主观情绪偏好。为此，本研究提出一种自适应、情感感知的新闻推荐框架，整合混合情感分析与强化学习。基于BBC新闻数据集，混合情感模型结合VADER、AFINN、TextBlob和SentiWordNet得分，生成稳健的文章级情感评估。新闻被分类为正面、负面或中性，其情感状态嵌入Q-learning架构中，指导智能体学习最优推荐策略。该系统能有效识别并推荐情绪匹配的文章，并通过迭代Q-learning持续优化个性化。结果表明，混合情感建模与强化学习结合，为以用户为中心的新闻推荐提供了可行、可解释且自适应的方法。
原文摘要 · Abstract (English)
News recommendation systems rely on automated sentiment analysis to personalise content and enhance user engagement. Conventional approaches often struggle with ambiguity, lexicon inconsistencies, and limited contextual understanding, particularly in multi-source news environments. Existing models typically treat sentiment as a secondary feature, reducing their ability to adapt to users' affective preferences. To address these limitations, this study develops an adaptive, sentiment-aware news recommendation framework by integrating hybrid sentiment analysis with reinforcement learning. Using the BBC News dataset, a hybrid sentiment model combines VADER, AFINN, TextBlob, and SentiWordNet scores to generate robust article-level sentiment estimates. Articles are categorised as positive, negative, or neutral, and these sentiment states are embedded within a Q-learning architecture to guide the agent in learning optimal recommendation policies. The proposed system effectively identifies and recommends articles with aligned emotional profiles while continuously improving personalisation through iterative Q-learning updates. The results demonstrate that coupling hybrid sentiment modelling with reinforcement learning provides a feasible, interpretable, and adaptive approach for user-centred news recommendation.
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