Sentimental preference extraction from online reviews for recommendation

Nieqing Cao, Jingjing Cao*, Panpan Liu, Wenfeng Li

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)

Abstract

With booming electronic commerce, online reviews are often created by users like who buys a product or goes to a restaurant. However, littery and unordered free-text reviews make it difficult for new users to acquire and analyze useful information. Thus, recommendation system plays an increasingly important role in online surfing. Nowadays, it has been proved that recommendation system based on topics is an available method in the theory and practice. However, there is little study to extract preferences from the perspective of sentiment. The method we proposed is to combine the topics and sentiments for generating a user’s preference from the user’s previous reviews. According to the degree of similarity with public’s preference, recommendation system we proposed would judge whether it should recommend the new products to this user. The empirical results show that the recommendation system we proposed can make accurately and effectively recommend.

Original languageEnglish
Title of host publicationInternet and Distributed Computing Systems - 8th International Conference, IDCS 2015, Proceedings
EditorsGiuseppe Di Fatta, Mukaddim Pathan, Giancarlo Fortino, Antonio Guerrieri, Frederic Stahl, Wenfeng Li
PublisherSpringer Verlag
Pages294-303
Number of pages10
ISBN (Print)9783319232362
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event8th International Conference on Internet and Distributed Computing Systems, IDCS 2015 - Windsor, United Kingdom
Duration: 2 Sept 20154 Sept 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9258
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Internet and Distributed Computing Systems, IDCS 2015
Country/TerritoryUnited Kingdom
CityWindsor
Period2/09/154/09/15

Keywords

  • Recommendation system
  • Sentiment analysis
  • Topic extraction
  • User preference

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