Keyphrases are clustered based on their distributional and lexical properties, and a hidden topic model is applied to the document text.

During training, we learn a hidden topic model from the text; each topic is also asso-

— probability of selecting 77 instead of ¢ — selects between 77 and ¢ for word topics — document topic model

The hidden topic model of the review text is used to determine the properties that a document as a whole supports.

Bayesian Topic Modeling One aspect of our model views properties as distributions over words in the document.

This approach is inspired by methods in the topic modeling literature, such as Latent Dirichlet Allocation (LDA) (Blei et al., 2003), where topics are treated as hidden variables that govern the distribution of words in a text.

Recent work has examined coupling topic models with explicit supervision (Blei and McAuliffe, 2007; Titov and McDonald, 2008).

Distributions of words in each topic were estimated as the proportion of words assigned to each topic, taking into account topic model priors Bgl and Bloc.

Before applying the topic models we removed punctuation and also removed stop words using the standard list of stop words,8 however, all the words and punctuation were used in the sentiment predictors.

To combat this problem we first train the sentiment classifiers by assuming that pygm is equal for all the local topics, which effectively ignores the topic model .

The model is at heart a topic model in that it assigns words to a set of induced topics, each of which may represent one particular aspect.

For example, other topic models can be used as a part of our model and the proposed class of models can be employed in other tasks beyond sentiment summarization, e.g., segmentation of blogs on the basis of topic labels provided by users, or topic discovery on the basis of tags given by users on social bookmarking sites.3

Text excerpts are usually extracted through string matching (Hu and Liu, 2004a; Popescu and Etzioni, 2005), sentence clustering (Gamon et al., 2005), or through topic models (Mei et al., 2007; Titov and McDonald, 2008).

String extraction methods are limited to fine-grained aspects whereas clustering and topic model approaches must resort to ad-hoc means of labeling clusters or topics.

Recently, Blei and McAuliffe (2008) proposed an approach for joint sentiment and topic modeling that can be viewed as a supervised LDA (sLDA) model that tries to infer topics appropriate for use in a given classification or regression problem.

A simple way to implement this idea is by re-ranking our k-best list according to a topic model estimated from the entire document collection.

Specifically, we use Latent Dirichlet Allocation (LDA) as our topic model (Blei et al., 2003).

An advantage of using LDA is that at test time we can perform inference without retraining the topic model .