Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder (2020)

Abstract: Detecting public sentiment drift is a challenging task due to sentiment change over time. Existing methods first build a classification model using historical data and subsequently detect drift if the model performs much worse on new data. In this paper, we focus on distribution learning by proposing a novel Hierarchical Variational Auto-Encoder (HVAE) model to learn better distribution representation, and design a new drift measure to directly evaluate distribution changes between historical data and new data. Our experimental results demonstrate that our proposed model achieves better results than three existing state-of-the-art methods.

## introduction public sentiments, whose information hidden in a temporal sequence of documents, become increasingly valuable for real-world applications. especially, identifying when public drifts occur is of great importance to different stakeholders, such as government agencies, companies and news agencies, where they can take proactive actions to avoid damages and pay close attentions to new topics/sentiments etc @xcite . however, the dynamic nature of drifts/changes makes it a challenging problem, although concept drift analysis can be applied to focus on detecting variation of data distributions over time. given historical data and new incoming data, how to accurately detect drift based on distributional change is a critical issue. while existing methods are proposed for sentiment analysis @xcite ; @xcite ; @xcite ; @xcite ; @xcite , etc., many of them are not designed for sentiment drift detection task. @xcite focuses on polarity shift detection, but it is at document-level instead of multi-document (or public) level. recently, some research have been conducted for stream sentiment classification @xcite . in particular, statistic processing control (spc) @xcite ; @xcite built a detection mechanism which accumulates statistic information of drift indications, and @xcite applied the mechanism in sentiment drift detection. in addition, @xcite proposed adwin method, which used the upper bound of hoeffding's inequality to mark drifts. additionally, nguyen et al. (2018) combined adwin with variational inference and built an online classification system. @xcite proposed an opinion drift detection method which is thresholdbased, restricting its applications. a novel framework, proposed by @xcite , contains an opinion shift detector based on kl-divergence, while its detection performance is affected by its labeling results. @xcite defined a novel concept of opinion contradictions and used it in a sentiment change detection experiment. however, the pair-wise method does not involve much history information. we observe most of the above methods indirectly detect drifts instead of directly evaluate distribution difference, leading to less effective results. the variational auto-encoder (vae) model, proposed by @xcite , is capable to learn latent distributions of inputs, and has better generalization performance @xcite . as such, we propose a novel hierarchical variational auto-encoder (hvae) model to tackle sentiment drift problem. in particular, we take sentiment distribution changes as the drifts. practically, sentiments are represented into a 2d vector, whose dimensions and values correspond to polarities (positive or negative) and corresponding intensities respectively. our main contributions can be summarized as follows: ## methodology we now introduce our proposed methodology, including hvae model and drift measure. ## experiments we have conducted extensive experiments to evaluate our proposed hvae model. ## conclusions to tackle challenges in sentiment drift detection, we have proposed a novel hvae model, which is 3-level meta-distributions to extend vae over hierarchical structure, leading to effective learning latent distribution representations of input sentiments. in addition, a new drift measure is designed to effectively measure distribution difference between historical and newly arrived data. different from existing classifier and threshold based models, the proposed method directly measures the distribution differences and thus is more effective. finally, extensive experimental results demonstrate that hvae performs significantly better than three state-of-the-art techniques across two benchmark datasets, indicating that it can be effectively used for real-world public sentiment drift analysis.