Summarizing user-generated reviews in digital libraries: a visual clustering approach

ABSTRACT

In this paper, we describe a visual clustering approach to summarizing user-generated reviews of digital library items and services. The approach consists of the steps of sentence extraction, aspect identification, opinion classification, and review summarization. Our work augments existing work by considering non-standard input and by incorporating clustering and visualization in summarization.

REFERENCES

• 1: J. Wiebe, T. Wilson, and C. Cardie. 2005. Annotating expressions of opinions and emotions in language. Language Resources and Evalution (formerly Computers and the Humanities), 1(2).

INDEX TERMS

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

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process; Information filtering
      H.3.7 Digital Libraries
          Subjects: Systems issues

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

General Terms:
Algorithms

Keywords:
aspect analysis, clustering, sentiment analysis, text classification, text summarization, user-generated review, visualization