Semi-supervised Category-specific Review Tagging on I ndonesian E -Commerce Product Reviews (2020)

Abstract: Product reviews are a huge source of natural language data in e-commerce applications. Several millions of customers write reviews regarding a variety of topics. We categorize these topics into two groups as either “category-specific” topics or as “generic” topics that span multiple product categories. While we can use a supervised learning approach to tag review text for generic topics, it is impossible to use supervised approaches to tag category-specific topics due to the sheer number of possible topics for each category. In this paper, we present an approach to tag each review with several product category-specific tags on Indonesian language product reviews using a semi-supervised approach. We show that our proposed method can work at scale on real product reviews at Tokopedia, a major e-commerce platform in Indonesia. Manual evaluation shows that the proposed method can efficiently generate category-specific product tags.

## related work while we can use a supervised learning approach to get generic topics from product reviews, it is impossible to use supervised approaches with "categoryspecific" topics due to the sheer number of possible topics for each product category. therefore, we use an unsupervised method to extract topics from product reviews in this paper. one of the earliest unsupervised method to extract keywords from text is the statistics based method. frequency or term frequency -inverse document frequency (tf-idf) score is calculated on the n-grams of all the reviews. the n-grams with higher score will be extracted as tags. graph-based methods @xcite can also used to extract keywords, where each token is a vertex and an edge is defined when two tokens are in the same context window. both methods however, fail to group n-grams of similar meaning together. latent dirichlet allocation (lda) @xcite and it's variants @xcite are popular methods to group words into topics. however lda processes a document as a bag of words with the assumption that each word is independent of each other. therefore this method loses valuable occurrence information. clustering method like k-means, dbscan can group similar words based on word embedding. however, word embedding is high dimensional data and clustering fails to work well on it due to the curse of dimensionality. a neural network model was proposed by @xcite to group phrases into topics. it overcomes the drawbacks of lda and clustering methods by utilizing the embedding information with attention mechanism to attend to important tokens in the sentence. we use this model in this paper. ## category-specific tag generation approach in this section we describe how the categoryspecific topic and the product tags are generated. the pipeline is shown in figure . 1. ## experimental setup in this section, we apply our proposed method to product reviews from tokopedia. we demonstrate the experimental results, and show the evaluation results of the generated category-specific topics. ## conclusion in this paper, we described a pipeline for categoryspecific review tagging using phrase extraction, topic generation, category-specific topic filtering and tag generation. given the product reviews, the pipeline generates the category-specific tags for each product and customers can filter product reviews with these tags. the pipeline is being implemented on product reviews at tokopedia, and proved to be successful when scaled to large number of reviews. we also evaluated the quality of the generated category-specific topics with manual labeling and results show that the pipeline can generate coherent category-specific topics.