Learning Attitudes and Attributes from Multi-Aspect Reviews

Abstract: The majority of online reviews consist of plain-text feedback together with a single numeric score. However, there are multiple dimensions to products and opinions, and understanding the aspects' that contribute to users' ratings may help us to better understand their individual preferences. For example, a user's impression of an audiobook presumably depends on aspects such as the story and the narrator, and knowing their opinions on these aspects may help us to recommend better products. In this paper, we build models for rating systems in which such dimensions are explicit, in the sense that users leave separate ratings for each aspect of a product. By introducing new corpora consisting of five million reviews, rated with between three and six aspects, we evaluate our models on three prediction tasks: First, we use our model to uncover which parts of a review discuss which of the rated aspects. Second, we use our model to summarize reviews, which for us means finding the sentences that best explain a user's rating. Finally, since aspect ratings are optional in many of the datasets we consider, we use our model to recover those ratings that are missing from a user's evaluation. Our model matches state-of-the-art approaches on existing small-scale datasets, while scaling to the real-world datasets we introduce. Moreover, our model is able todisentangle' content and sentiment words: we automatically learn content words that are indicative of a particular aspect as well as the aspect-specific sentiment words that are indicative of a particular rating.

• The paper's main contribution is the introduction of PALLA, a model that efficiently predicts multi-aspect ratings from review texts.
• It employs unsupervised, semi-, and fully-supervised regimes to disentangle aspect-specific content from sentiment cues for clear interpretability.
• The model demonstrates robust scalability on datasets with over five million reviews, enhancing personalized recommendation systems.

Learning Attitudes and Attributes from Multi-Aspect Reviews

The paper, "Learning Attitudes and Attributes from Multi-Aspect Reviews," by Julian McAuley, Jure Leskovec, and Dan Jurafsky, addresses the challenge of interpreting and leveraging multi-aspect ratings from user reviews to better capture user preferences and sentiments. Their approach is evaluated on extensive datasets from domains such as BeerAdvocate, Amazon, and Audible, consisting of five million reviews. This paper introduces a model termed Preference and Attribute Learning from Labeled Groundtruth and Explicit Ratings (PALLA), which has been designed to understand and model the complex interplay between content and sentiment within multi-aspect review data.

The researchers' primary aim was to effectively utilize user-provided aspect ratings, where users leave separate ratings for different product attributes, to train models capable of predicting these ratings from the review text. This task required addressing three intertwined challenges: identifying which sentences in reviews correspond to which aspects, summarizing reviews using the most informative sentences, and inferring missing aspect ratings from available data.

Key to their approach is the ability to disentangle words indicative of an aspect from those pointing to sentiment in these reviews. This dual modeling ensures clarity and interpretability in aspect-specific lexicons. For instance, content words like “body” or “flavor” are associated with specific aspects like “palate” or “taste” in the context of beer reviews, while sentiment words like “warm” can convey negative or positive sentiments depending on the aspect under discussion.

The authors demonstrate the scalability and realism of their model using a newly introduced dataset compiled from large corpora, distinguishing it from prior studies that relied mainly on smaller datasets. Empirically, their model matched state-of-the-art performances while handling much larger datasets, indicating robustness and scalability. They propose three training regimes—unsupervised, semi-supervised, and fully-supervised—highlighting that even unsupervised techniques, when utilizing large datasets, can yield satisfactory performance and interpretability.

Significant findings include the model’s capability to successfully predict aspect ratings even when user reviews convey conflicting sentiments across different aspects. Importantly, this was achieved by modeling aspect correlations, thus surpassing simpler models that either do not use text-data segmentation or do not account for correlations between aspects.

In long-term practical implications, the model could enhance personalized recommendation systems by understanding deeper user sentiments and preferences across various aspects. Future research directions could involve refining these models to consider more complex feedback structures, including interactions beyond text and ratings, such as user profiles and behavioral data. Further advancements may also explore automated feature extraction from other media types, such as images and audio, to complement and enhance the existing textual datasets in forming even more comprehensive models of consumer sentiment and preference. Additionally, expanding the dataset to include diverse and less explored product categories could reveal aspect-specific language nuances and preferences in other marketplaces.

Overall, the paper contributes significantly to the automated understanding of online reviews, advancing models that are not only robust and scalable but also highly interpretable. It underscores the value of integrating explicit aspect ratings into sentiment analysis and pushes the boundaries of application across various domains in the real world.