Automatic Aggregation by Joint Modeling of Aspects and Values
This work addresses the need for automated review summarization for consumers and businesses, though it is incremental as it builds on existing joint modeling approaches.
The paper tackles the problem of aggregating product reviews by jointly identifying aspects and analyzing sentiment, enabling discovery of highly-rated or inconsistent aspects. It demonstrates that the model outperforms baselines with up to 32% relative error reduction on aspect identification and up to 20% on sentiment analysis.
We present a model for aggregation of product review snippets by joint aspect identification and sentiment analysis. Our model simultaneously identifies an underlying set of ratable aspects presented in the reviews of a product (e.g., sushi and miso for a Japanese restaurant) and determines the corresponding sentiment of each aspect. This approach directly enables discovery of highly-rated or inconsistent aspects of a product. Our generative model admits an efficient variational mean-field inference algorithm. It is also easily extensible, and we describe several modifications and their effects on model structure and inference. We test our model on two tasks, joint aspect identification and sentiment analysis on a set of Yelp reviews and aspect identification alone on a set of medical summaries. We evaluate the performance of the model on aspect identification, sentiment analysis, and per-word labeling accuracy. We demonstrate that our model outperforms applicable baselines by a considerable margin, yielding up to 32% relative error reduction on aspect identification and up to 20% relative error reduction on sentiment analysis.