Monitoring online customer reviews is important for business organisations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organised in temporally-ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.
翻译：在线客户审查监测对于商业组织衡量客户满意度和更好地管理其声誉非常重要。 在本文中,我们提出了一个新的动态品牌-托盘模型(dBTM),该模型能够自动检测和跟踪在按时间顺序间隔安排的产品审查中出现的与品牌有关的情绪分数和极化主题。 dBTM模型,高森州空间模型的潜在品牌极化分数的演变和一段时间内专题词分布的演变。它还包括一项元学习战略,以控制每个时间间隔内主题字分布的更新,以确保主题的平稳过渡和更好的品牌分数预测。它是根据从化妆片Alley审查和酒店审查数据集中构建的数据集进行评估的。实验结果表明,dBTM在品牌排名方面超越了若干竞争性基线,实现了主题一致性和独特性的良好平衡,并每隔一段时间提取了极地分化的极化专题。
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