Rating prediction using feature words extracted from customer reviews

Masanao Ochi, Makoto Okabe, Rikio Onai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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 sparse-ness, our method improves prediction accuracy as measured using RankLoss.

Original languageEnglish
Title of host publicationSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages1205-1206
Number of pages2
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11 - Beijing
Duration: 2011 Jul 242011 Jul 28

Other

Other34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11
CityBeijing
Period11/7/2411/7/28

Keywords

  • Rating prediction
  • Review mining
  • Sentiment analysis

ASJC Scopus subject areas

  • Information Systems

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