Modeling Online Reviews with Multi-grain Topic Models (0801.1063v1)

Abstract: In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., waitress' andbartender' are part of the same topic `staff' for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.

• The paper introduces MG-LDA, which distinguishes between global product features and local ratable aspects in reviews.
• It uses a sliding-window technique and collapsed Gibbs sampling to efficiently extract meaningful topics from noisy user data.
• MG-LDA outperforms traditional models by reducing ranking loss from 0.735 to 0.706 in a hotel review dataset.

Modeling Online Reviews with Multi-grain Topic Models

In "Modeling Online Reviews with Multi-grain Topic Models," Titov and McDonald propose an innovative approach for extracting ratable aspects from user-generated online reviews. Traditional sentiment analysis models, particularly those based on Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA), fall short due to their tendency to produce topics that reflect global properties of a product, such as brand or location, rather than user-related ratable aspects. The authors introduce Multi-grain LDA (MG-LDA) to address these limitations, effectively distinguishing between local and global topics.

Methodology

The proposed MG-LDA framework builds upon the standard LDA and PLSA by incorporating multi-grain topics. Here, global topics are representative of overarching properties of the product or service, while local topics capture finer-grained aspects that users typically rate. By integrating a sliding-window approach over text, the MG-LDA model allows for the dynamic adjustment of local context, which is essential for distinguishing and clustering ratable aspects.

The inference in MG-LDA is conducted using a collapsed Gibbs sampling algorithm, allowing for the efficient computation of the necessary probabilities. This approach significantly enhances the model's ability to extract meaningful topics even from noisy, user-generated content.

Experimental Validation

The paper presents qualitative and quantitative evaluations to demonstrate the advantages of the MG-LDA model. Three datasets are explored qualitatively: reviews of MP3 players, hotels, and restaurants. For each dataset, the model's local topics (e.g., battery life for MP3 players or cleanliness and location for hotels) are contrasted with standard LDA topics. The results illustrate the MG-LDA model's superior capability to produce coherent and ratable aspects.

Quantitatively, the efficacy of MG-LDA is tested using a multi-aspect rating task with the PRanking algorithm on a dataset of 27,564 hotel reviews from TripAdvisor. Features extracted from both LDA and MG-LDA are included in the input to the PRanking model. The inclusion of MG-LDA features consistently outperforms the baseline and LDA-enhanced models in terms of ranking loss, particularly with unigram features alone (0.706 for MG-LDA versus 0.735 for LDA). These improvements signify that MG-LDA captures ratable aspects more accurately, making it more effective for this task.

Implications and Future Directions

The MG-LDA model holds significant implications for sentiment analysis and opinion mining. By extracting coherent and ratable aspects, it provides valuable insights into user opinions not possible with traditional models. This has practical applications in generating more informative opinion summaries and facilitating more nuanced sentiment analysis across various domains.

Theoretically, the introduction of multi-grain topics could be expanded to richer hierarchical structures, accommodating more complex data and latent aspects. Future developments could include supervised versions of MG-LDA to leverage labeled data for enhanced performance in specific tasks, such as multi-aspect classification. Further integration with sentiment classifiers also presents an exciting avenue, potentially paving the way for more sophisticated joint models for sentiment and topic analysis.

In summary, "Modeling Online Reviews with Multi-grain Topic Models" contributes a robust methodological advancement in the field of sentiment analysis, addressing critical gaps in existing models and offering promising directions for both applied and theoretical research.