Rating prediction using feature words extracted from customer reviews

Authors:
Masanao Ochi;Makoto Okabe;Rikio Onai
Affiliations:
The University of Electro-Communications, Tokyo, Japan;The University of Electro-Communications, Tokyo, Japan;The University of Electro-Communications, Tokyo, Japan
Venue:
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Year:
2011

Citing 1
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Abstract

We developed a simple method of improving the accuracy of rating prediction using feature words extracted from customer reviews. Many rating predictors work well for a small and dense dataset of customer reviews. However, a practical dataset tends to be large and sparse, because it often includes too many products for each customer to buy and evaluate. Data sparseness reduces prediction accuracy. To improve accuracy, we reduced the dimension of the feature vector using feature words extracted by analyzing the relationship between ratings and accompanying review comments instead of using ratings. We applied our method to the Pranking algorithm and evaluated it on a corpus of golf course reviews supplied by a Japanese e-commerce company. We found that by successfully reducing data sparseness, our method improves prediction accuracy as measured using RankLoss.