|Fig. 1 Rating and review model in a per-aspect way. It consists of two parts: modeling ratings and modeling reviews.|
1. A background language component;
2. A background sentiment language component;
3. A movie-specific language component;
4. An aspect-specific language component;
5. An aspect-specific sentiment language component.
Fig.1 illustrate the entire framework of their probabilistic model. User's interest and movie's relevance are collectively used to model the final rating and review contents. Their model is called "JMARS".
Experiments: The experiment is conducted on a dataset collected from IMDb - a famous movie review website. In total, 50k movies along with their reviews are crawled. They use 80% of data as training data, 10% as validation and 10% for testing. Fig. 2 shows comparison of JMARS in terms of perplexity. When factor size is set as 5 or 10 (5 or 10 aspects are considered in movie), JMARS could always outperforms HFT approach. Besides, Fig. 3 reveals MSE comparison which also proves JMARS good performance.
|Fig. 2 Comparison of models using perplexity.|
|Fig. 3 Comparison of models in terms of MSE.|