Article Structure
Abstract
We present a probabilistic topic model for jointly identifying properties and attributes of social media review snippets.
Introduction
Online product reviews have become an increasingly valuable and influential source of information for consumers.
Related Work
Our work on review aggregation has connections to three lines of work in text analysis.
Problem Formulation
In this section, we discuss the core random variables and abstractions of our model.
Model
Our model generates the words of all snippets for each product in a collection of products.
Inference
The goal of inference is to predict the snippet property and attribute distributions over .
Experiments
In this section, we describe in detail our data set and present three experiments and their results.
Conclusion
We have presented a probabilistic topic model for identifying properties and attitudes of product review snippets.
Topics
baseline system
- While MUC has a deficiency in that putting everything into a single cluster will artificially inflate the score, parameters on our model are set so that the model uses the same number of clusters as the baseline system .Page 7, “Experiments”
- While it would be possible to artificially inflate the score by putting everything into a single cluster, the parameters on our model and the likelihood objective are such that the model prefers to use all available clusters, the same number as the baseline system .Page 7, “Experiments”
- While our system does suffer on precision in comparison to the baseline system , the recall gains far outweigh this loss, for a total error reduction of 20% on the MUC measure.Page 7, “Experiments”
- The most common cause of poor cluster choices in the baseline system is its inability to distinguish property words from attribute words.Page 7, “Experiments”
- As in the cluster prediction case, the main flaw with the DISCRIMINATIVE baseline system is its inability to recognize which words are relevant for the task at hand, in this case the attribute words.Page 8, “Experiments”
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topic model
- We present a probabilistic topic model for jointly identifying properties and attributes of social media review snippets.Page 1, “Abstract”
- We capture this idea using a Bayesian topic model where a set of properties and corresponding attribute tendencies are represented as hidden variables.Page 2, “Introduction”
- Finally, a number of approaches analyze review documents using probabilistic topic models (Lu and Zhai, 2008; Titov and McDonald, 2008; Mei et al., 2007).Page 2, “Related Work”
- We have presented a probabilistic topic model for identifying properties and attitudes of product review snippets.Page 9, “Conclusion”
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fine-grained
- Specifically, we are interested in identifying fine-grained product properties across reviews (e.g., battery life for electronics or pizza for restaurants) as well as capturing attributes of these properties, namely aggregate user sentiment.Page 1, “Introduction”
- While our model captures similar high-level intuition, it analyzes fine-grained properties expressed at the snippet level, rather than document-level sentiment.Page 3, “Related Work”
- Property: A property corresponds to some fine-grained aspect of a product.Page 3, “Problem Formulation”
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unigram
- Global Distributions: At the global level, we draw several unigram distributions: a global background distribution 63 and attribute distributions 6% for each attribute.Page 3, “Model”
- Product Level: For the ith product, we draw property unigram distributions 6351, .Page 3, “Model”
- The DISCRIMINATIVE baseline for this task is a standard maximum entropy discriminative binary classifier over unigrams .Page 7, “Experiments”
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