Automatic Restaurant Information and Keyword Extraction by Mining Blog Data for Chinese Restaurant Search

Chien Li Chou, Min-Ho Tsai, Chien-Ho Chao, Hsiao-Jung Lin, Hua-Tsung Chen, Suh-Yin Lee, Chien Peng Ho

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

1 Scopus citations

Abstract

Restaurant search and recommendation system is a very popular service in many countries. In those systems, most of the restaurant information such as restaurant name, address, phone number, and introduction are collected manually. In this paper, we propose a restaurant information extraction method which can automatically extract restaurant information from online reviews of restaurants in blogs. In addition, by calculating TFIDFs of words in blog posts, the hot keywords can be discovered and ranked. For restaurant search, users are allowed to search by keywords, areas, and/or extracted hot keywords. The experimental results show that the proposed method can achieve over 90 % average accuracy of hot keyword extraction and about 95 % mean average precision for restaurant search. In user study, the fact that the proposed system is more useful than Google search in restaurant search is presented.
Original languageEnglish
Title of host publication18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
Pages700-711
Number of pages12
DOIs
StatePublished - 2014

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
PublisherSpringer Verlag
ISSN (Print)0302-9743

Keywords

  • Information retrieval; Opinion mining; TFIDF; Food and restaurants; Restaurant search

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