Tracking Brand-Associated Polarity-Bearing Topics in User Reviews (2023)

Abstract: Monitoring online customer reviews is important for business organizations 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 organized 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.1

## introduction with the increasing popularity of social media platforms, customers tend to share their personal experience towards products online. tracking customer reviews online could help business organizations to measure customer satisfaction and better manage their reputations. monitoring brandassociated topic changes in reviews can be done through the use of dynamic topic models @xcite @xcite . approaches such as the dynamic joint sentiment-topic (djst) model @xcite are able to extract polarity-bearing topics evolved over time by assuming the dependency of the sentiment-topic-word distributions across time slices. they require the incorporation of word prior polarity information, however, and assume topics are associated with discrete polarity categories. furthermore, they are not able to infer brand polarity scores directly. a recently proposed brand-topic model (btm) @xcite is able to automatically infer real-valued brand-associated sentiment scores from reviews and generate a set of sentiment-topics by gradually varying its associated sentiment scores from negative to positive. this allows users to detect, for example, strongly positive topics or slightly negative topics. btm, however, assumes all documents are available prior to model learning and cannot track topic evolution and brand polarity changes over time. in this paper, we propose a novel framework inspired by meta-learning, which is widely used for distribution adaptation tasks @xcite . when training the model on temporally ordered documents divided into time slice, we assume that extracting polarity-bearing topics and inferring brand polarity scores in each time slice can be treated as a new sub-task and the goal of model learning is to learn to adapt the topic-word distributions associated with different brand polarity scores in a new time slice. we use btm as the base model and store the parameters learned in a memory. at each time slice, we gauge model performance on a validation set based on the modelgenerated brand ranking results. the evaluation results are used for early stopping and dynamically initializing model parameters in the next time slice with meta learning. the resulting model is called dynamic brand topic modeling (dbtm). the final outcome from dbtm is illustrated in figure 1 , in which it can simultaneously track topic evolution and infer latent brand polarity score changes over time. moreover, it also enables the generation of fine-grained polarity-bearing topics in each time slice by gradually varying brand polarity scores. in essence, we can observe topic figure 1 : brand-associated polarity-bearing topics tracking by our proposed model. we show top words from an example topic extracted in time slice 1, 4, and 8 along the horizontal axis. in each time slice, we can see a set of topics generated by gradually varying their associated sentiment scores from -1 (negative) to 1 (positive) along the vertical axis. for easy inspection, positive words are highlighted in blue while negative ones in red. we can observe in time 1, negative topics are mainly centred on the complaint of the chemical smell of a perfume, while positive topics are about the praise of the look of a product. from time 1 to time 8, we can also see the evolving aspects in negative topics moving from complaining about the strong chemical of perfume to overpowering sweet scent. in the lower part of the figure, we show the inferred polarity scores of three brands. for example, chanel is generally ranked higher than lancôme, which in turn scores higher than the body shop. we have evaluated dbtm on a review dataset constructed from makeupalley, foot_2 consisting of over 611k reviews spanning over 9 years, and a hotel review dataset sampled from hotelrec @xcite , containing reviews of the most popular 25 hotels over 7 years. we compare its performance with a number of competitive baselines and observe that it generates better brand ranking results, predicts more accurate brand score time series, and produces well-separated polarity-bearing topics with more balanced topic coherence and diversity. more interestingly, we have evaluated dbtm in a more difficult setup, where the supervised label information, that is, review ratings, is only supplied in the first time slice, and afterwards, dbtm is trained in an unsupervised way without the use of review ratings. dbtm under such a setting can still produce brand ranking results across time slices more accurately compared to baselines trained under the supervised setting. this is a desirable property as dbtm, initially trained on a small set of labeled data, can self-adapt its parameters with streaming data in an unsupervised way. our contributions are three-fold: our work is related to the following research: ## preliminary: brand topic model the brand-topic model (btm) @xcite , as shown in the middle part of figure 2 , is trained on review documents paired with their document-level sentiment class labels (e.g., 'positive', 'negative', and 'neutral'). it can automatically infer real-valued brand-associated polarity scores and generate fine-grained sentiment-topics in which a continuous change of words under a certain topic can be observed with a gradual change of its associated sentiment. it was partly inspired by the text-based ideal point (tbip) model @xcite , which aims to model the generation of text via poisson factorization. in particular, for the input bag of words data, the count for term v in document d is formulated as term count c dv , which is assumed to be sampled from a poisson distribution c dv ∼ poisson(λ dv ) where the rate parameter λ dv can be factorized as: here, θ dk denotes the per-document topic intensity, and β kv represents the topic-word distribution. we have θ ∈ r d×k + , β ∈ r k×v + , where d is the total number of documents in a corpus, k is the topic number, and v is the vocabulary size. then, brand-polarity score x b d and topic-word offset η kv are added to the model: the intuition behind the above formulation is that the latent variable x b d , which captures the brand polarity score, can be either positive or negative. if a word tends to frequently occur in reviews with positive polarities, but the polarity score of the current brand is negative, then the occurrence count of such a word would be reduced by making x b d and η kv to have opposite signs. a gamma prior is placed on θ and β, with a, b, c, d being hyperparameters, while a normal prior is placed over the brand polarity score x and the topic-word count offset η. btm makes use of gumbel-softmax @xcite to construct document features for sentiment classification. this is because directly sampling word counts from the poisson distribution is not differentiable. gumbel-softmax, which is a gradient estimator with the reparameterization trick, is used to enable back-propagation of gradients. more details can be found in @xcite . ## dynamic brand topic model (dbtm) to track brand-associated topic dynamics in customer reviews, we split the documents into time slices where the time period of each slice can be set arbitrarily at, for example, a week, a month, or a year. in each time slice, we have a stream of m documents {d 1 , • • • , d m } ordered by their publication timestamps. a document d at time slice t is input as a bag-of-words representation. we extend btm to deal with streaming documents by assuming that documents at the current time slice are influenced by documents at past. the resulting model is called dynamic brand-topic model (dbtm), with its architecture illustrated in figure 2 . ## experimental setup datasets popular datasets such as yelp and amazon products @xcite and multi-domain sentiment dataset @xcite are constructed by randomly selecting reviews from amazon or yelp without considering their distributions over various brands and across different time periods. therefore, we construct our own dataset by crawling reviews from top 25 brands from makeupalley, a review website on beauty products. each review is accompanied with a rating score, product type, brand, and post time. we consider reviews with the ratings of 1 and 2 as the negative class, those with the rating of 3 as the neutral class, and the remaining with the ratings of 4 and 5 as the positive class, following the label setting in btm. the entire dataset contains 611,128 reviews spanning over 9 years @xcite . we treat each year as a time slice and split reviews into 9 time slices. the average review length is 123 words. besides the makeupalley-beauty, we also run our experiments on hotelrec @xcite , by selecting reviews from the top 25 hotels over 7 years (2012 to 2018). the statistics of our datasets are shown in ## experimental results in this section, we present the experimental results in comparison with the baseline models in brand rating, topic coherence/uniqueness measures, and qualitative evaluation of generated topics. for fair comparison, baselines are trained based on all previous time slices and predict on the current time slice. ## conclusion we have presented dbtm, which is able to automatically detect and track brand-associated topics and sentiment scores. experimental evaluation based on the reviews from makeupalley and hotelrec demonstrates the superiority of dbtm over previous models in brand ranking and dynamic topic extraction. the variant of dbtm, o-dbtm, trained with document-level sentiment labels in the first time slice only, outperforms baselines in brand ranking and achieves the best overall result in topic quality evaluation. this shows the effectiveness of the proposed architecture in modeling the evolution of brand scores and topics across time intervals. our model currently only considers review ratings, but real-world applications potentially involve additional factors (e.g., user preference). a possible solution is to explore simultaneous modeling of user preferences to extract personalised brand polarity topics.