A random walk on the red carpet: rating movies with user reviews and pagerank

ABSTRACT

Although PageRank has been designed to estimate the popularity of Web pages, it is a general algorithm that can be applied to the analysis of other graphs other than one of hypertext documents. In this paper, we explore its application to sentiment analysis and opinion mining: i.e. the ranking of items based on user textual reviews. We first propose various techniques using collocation and pivot words to extract a weighted graph of terms from user reviews and to account for positive and negative opinions. We refer to this graph as the sentiment graph. Using PageRank and a very small set of adjectives (such as 'good', 'excellent', etc.) we rank the different items. We illustrate and evaluate our approach using reviews of box office movies by users of a popular movie review site. The results show that our approach is very effective and that the ranking it computes is comparable to the ranking obtained from the box office figures. The results also show that our approach is able to compute context-dependent ratings.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
opinion mining, pagerank, ranking