Source-linked AI summary
Modeling Online Reviews with Multi-grain Topic Models
Ivan Titov, Ryan McDonald
TL;DR
The paper addresses unsupervised extraction of ratable aspects from online reviews, where standard topic models often identify global item properties instead. It extends PLSA and LDA into multi-grain models with global and local topics, and reports more coherent aspect topics plus improved multi-aspect ranking.
Problem
Online opinion mining needs methods that identify the fine-grained aspects reviewers rate, rather than only assigning sentiment to entire reviews.
Method
The paper extends PLSA and LDA with multi-grain topic models that separate global topics from local topics selected through sliding-window context.
Results
The multi-grain models produce more coherent topics better correlated with ratable aspects and significantly improve multi-aspect ranking.
Takeaways & Limitations
Multi-grain topics can extract and cluster ratable aspects while distinguishing them from global properties of reviewed items.
Takeaways & Limitations
Sentence-level modeling has insufficient co-occurrence context, while topic-transition alternatives are considerably more computationally expensive.
Abstract
from arXiv · showhide
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' and `bartender' 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.
1 Introduction
The paper targets unsupervised extraction of ratable aspects from noisy online reviews, enabling fine-grained opinion analysis and aspect-based summaries. It extends LDA and PLSA with multi-grain topics and evaluates their qualitative coherence and quantitative utility.
- Motivation: Online reviews contain user-provided ratings but remain noisy, motivating tailored mining methods for products and services.
- Problem: Fine-grained sentiment analysis requires identifying frequently rated aspects before classifying and aggregating sentiment for each aspect.
- Approach: The paper focuses on unsupervised ratable-aspect extraction, extending PLSA and LDA because standard topics often classify product instances rather than aspects.
- Approach: Multi-grain models generate words from global or context-specific local topics, aiming to separate object properties from ratable aspects.
- Evaluation: The evaluation reports more coherent, aspect-correlated topics and significant improvement in multi-aspect ranking from multi-grained topic features.
2 Unsupervised Topic Modeling
Standard PLSA and LDA model document-level co-occurrences, which can produce global item topics instead of ratable aspects. MG-LDA addresses this by combining global topics with local, sliding-window topics that vary across a document.
- PLSA and LDA: PLSA represents each document as a mixture of latent topics, with topic-word and document-topic distributions estimated by maximum likelihood.Its parameters include ϕz=P(w|z), θd=P(z|d), and ρd=P(d).
- PLSA and LDA: PLSA can overfit as its parameter count grows with corpus size and cannot directly represent documents outside the training collection.LDA addresses the latter issue with a generative model for document-specific topic distributions.
- Limitations: Because LDA and PLSA use bag-of-words document-level co-occurrences, they tend to infer global topics such as hotel locations or product brands instead of ratable aspects.
- Limitations: Applying these models to sentences reduces the co-occurrence domain, while topic-transition models are more computationally expensive and still do not distinguish global from ratable topics.
- MG-LDA: MG-LDA models global topics fixed per document and local topics varying by context, sampling each word from either distribution.
- MG-LDA: MG-LDA uses overlapping sliding windows to enlarge the local co-occurrence domain without the expense of explicit topic-transition models.
3 Inference with MG-LDA
MG-LDA inference uses collapsed Gibbs sampling, integrating out topic-distribution parameters and sampling assignments for windows, contexts, and topics. The method factors the joint probability into word, window, context, and topic components, with counts defining the conditional updates; sampling is reported to converge in time similar to standard LDA.
- Inference setup: MG-LDA inference modifies an existing Gibbs-sampling algorithm for the model and can alternatively use EM or TEM with fixed uniform ψd,s.The alternative formulation is viewed as a generalization of the PLSA aspect model.
- Conditional sampling: The sampler assigns each word to a sliding window, a global or local context, and a topic, conditioning on assignments for all other words.The conditional distribution is defined over vd,i, rd,i, and zd,i while excluding the considered word's assignments.
- Collapsed inference: Integrating out ϕgl, ϕloc, ψ, π, θgl, and θloc yields count-based terms for word, sentence, window, context, and topic assignments.The counts include topic-word totals, sentence lengths, sentence-to-window assignments, and window-level global/local assignments.
- Probability factorization: The joint probability factors as P(w, v, r, z) = P(w|r, z)P(v, r, z), with P(v, r, z) further decomposed into P(v)P(r|v)P(z|r, v).This factorization separates word probabilities from window, context, and topic assignments.
- Computational behavior: Sampling with MG-LDA computes counts without the considered word's assignment and is reported to converge in time similar to standard LDA implementations.The resulting procedure is described as fast in practice.
- Inference limitation: Collapsed sampling prevents aggregating statistics over multiple probabilistic-model samples because topic indices lack correspondence across samples.The paper uses collapsed sampling in its experiments but notes that EM or variational approximations can be used for smaller collections.
4 Experiments
The experiments compare MG-LDA with standard LDA across product and service reviews, using qualitative topic coherence and quantitative multi-aspect ranking. MG-LDA generally identifies more ratable aspects and improves ranking performance, while short, cuisine-specific restaurant reviews remain challenging.
- Qualitative experiments: MG-LDA local topics corresponded to ratable aspects more closely than standard LDA topics across MP3-player and hotel reviews.For MP3 players, all 10 local topics appeared ratable, whereas only 4 of 40 LDA topics did; for hotels, all local topics were ratable compared with 9 of 45 LDA topics.
- Qualitative experiments: MG-LDA separated local ratable aspects from global topics representing brands, player types, hotel types, and locations.For MP3 players, most global topics represented brands or player categorizations; hotel global topics represented hotel types and locations.
- Qualitative experiments: Restaurant reviews challenged both models because they were short and their ratable aspects varied substantially by cuisine.The average restaurant review was 4.2 sentences, and vocabulary overlap across cuisine-specific aspects was limited.
- Quantitative experiments: The quantitative evaluation targets aspect ratings rather than held-out likelihood because the goal is correspondence between learned topics and ratable object aspects.Multi-aspect ranking predicts discrete numeric ratings for multiple aspects such as food, service, and decor.
- Qualitative experiments: MG-LDA with 15 local topics identified 9 ratable dimensions in Italian restaurant reviews, including wine, pizza, pasta, service, value, and atmosphere.The preliminary result covered wine, pizza, pasta, general food, location, service, waiting, value, and atmosphere.
- Quantitative experiments: Adding topic-model features improved multi-aspect ranking, with MG-LDA producing significantly lower loss than LDA features in the unigram experiment and retaining gains with richer features.The full feature experiment included unigrams, bigrams, and frequent trigrams; MG-LDA still significantly improved performance and outperformed LDA.
5 Related Work
Prior work spans unsupervised, weakly supervised, and supervised approaches to extracting and summarizing ratable aspects, while this paper’s topic-model approach is presented as complementary to them.
- Unsupervised methods: Unsupervised sentiment summarization methods include association mining, sentence clustering, and topic-based approaches.The surveyed methods differ in whether they extract explicit aspects, cluster sentences, or jointly model topics and sentiment.
- Unsupervised methods: Hu and Liu extract explicit product aspects but neither cluster them nor infer implicitly mentioned aspects such as hotel cleanliness.Their aspect definition is simply a string, limiting clustering and implicit-aspect discovery.
- Supervised and weakly supervised methods: Other systems use sentence clustering, user-supplied aspect hierarchies, or manually extracted and clustered aspects from labeled data.The supervised approach requires a labeled corpus for every domain of interest.
- Relationship to prior work: The proposed topic-model approach is orthogonal to most prior methods and can cluster explicit aspects or improve recall in ontology- or label-dependent systems.It can therefore complement knowledge-driven and supervised extraction approaches rather than replace them.
- Modeling choices: MG-LDA uses sliding windows for local topics instead of explicitly modeling topic transitions, reducing computational expense while retaining good results.The paper notes that alternative multi-grain models could combine n-gram local topics with document-level global topics.
6 Summary and Future Work
The paper concludes that multi-grain topic models outperform standard topic models for extracting ratable aspects by grouping important terms into coherent topics. Future work targets supervised, hierarchical, and joint aspect-sentiment models.
- Summary: Multi-grain topic models are superior to standard topic models for extracting ratable aspects from online reviews.The conclusion identifies coherent grouping as a central advantage over methods that identify important terms without adequate clustering.
- Summary: The models identify important terms and cluster them into coherent groups, addressing a deficiency of many previously proposed methods.This capability makes the models particularly suited to ratable-aspect extraction.
- Future work: Future work includes supervised models for multi-aspect classification and hierarchical topic models using available review datasets.TripAdvisor data is named as a prominent dataset for these investigations.
- Future work: The authors are investigating two-stage and joint models that combine aspect extraction with sentiment aggregation and summarization.The two-stage approach extracts aspects first and aggregates sentiment afterward, while TSM-style models would treat the tasks jointly.
- Modeling choices: MG-LDA’s overlapping-window design models transitions through sentence distributions rather than predetermined non-overlapping windows.The comparison contrasts this design with models that explicitly model transitions between non-overlapping windows.