Parametric and Non-parametric User-aware Sentiment Topic Models

  title={Parametric and Non-parametric User-aware Sentiment Topic Models},
  author={Zaihan Yang and Alexander Kotov and Aravind Mohan and Shiyong Lu},
The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in… CONTINUE READING
Highly Cited
This paper has 21 citations. REVIEW CITATIONS

17 Figures & Tables

Extracted Numerical Results

  • Performance of the model is evaluated in terms of perplexity, which is a measure derived from the likeli­hood of the data in the testing subset (10% of all reviews) under the model estimated on the training subset (90% of all reviews) of each dataset.



Citations per Year

Citation Velocity: 8

Averaging 8 citations per year over the last 3 years.

Learn more about how we calculate this metric in our FAQ.