| Christina Sauper | Aria Haghighi | Regina Barzilay |
|---|---|---|
| csauper@csail.mit.edu | aria42@gmail.com | regina@csail.mit.edu |
We present a probabilistic topic model for jointly identifying properties and attributes of social media review snippets. Our model simultaneously learns a set of properties of a product and captures aggregate user sentiments towards these properties. This approach directly enables discovery of highly rated or inconsistent properties of a product. Our model admits an efficient variational mean-field inference algorithm which can be parallelized and run on large snippet collections. We evaluate our model on a large corpus of snippets from Yelp reviews to assess property and attribute prediction. We demonstrate that it outperforms applicable baselines by a considerable margin.
Full Text: http://groups.csail.mit.edu/rbg/code/content_attitude/sauper-acl-11.pdf
This code is available for research use only.
All main code is in variational.rb. To run, you need a configuration file
(sample provided in params). Then, simply run with:
ruby variational.rb params
Each line of each data file should contain one snippet, optionally with parts of speech (can be automatically tagged). For example:
Their_PRP$ bread_NN basket_NN was_VBD very_RB good_JJ
Sample annotated files for testing are provided in annotation/.
aspect.json
Labels for aspect identification, represented as a clustering over
snippets.
words.json
Per-word labels for type of word:
- 0 -- aspect
- 1 -- sentiment
- 2 -- background
Collected via Amazon Mechanical Turk.
sentiment_{train,test}
Annotation of snippet sentiments, positive or negative. All neutral or
ambiguous snippets have been removed from this set.