Paper Info
Title
Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System.
Abstract
In this paper corpus-based information extraction and opinion mining method is proposed. Our domain is restaurant reviews, and our information extraction and opinion mining module is a part of a Russian knowledge-based recommendation system. Our method is based on thorough corpus analysis and automatic selection of machine learning models and feature sets. We also pay special attention to the verification of statistical significance. According to the results of the research, Naive Bayes models perform well at classifying sentiment with respect to a restaurant aspect, while Logistic Regression is good at deciding on the relevance of a user's review. The approach proposed can be used in similar domains, for example, hotel reviews, with data represented by colloquial non-structured texts (in contrast with the domain of technical products, books, etc.) and for other languages with rich morphology and free word order.
Year
DOI
Venue
2014
10.1007/978-3-319-11397-5_21
Lecture Notes in Computer Science
Keywords
Field
DocType
Information extraction,Opinion mining,Restaurant recommendation system,Machine learning
Recommender system,Information retrieval,Sentiment analysis,Computer science,Information extraction
Conference
Volume
ISSN
Citations 
8791
0302-9743
0
PageRank 
References 
Authors
0.34
25
3
Name
Order
Citations
PageRank
Ekaterina Pronoza102.70
Elena Yagunova235.18
Svetlana Volskaya300.68