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Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System. / Pronoza, E.; Yagunova, E.; Volskaya, S.

In: Lecture Notes in Computer Science, Vol. 8791, 2014, p. 272-284.

Research output: Contribution to journal › Article

Harvard

Pronoza, E, Yagunova, E & Volskaya, S 2014, 'Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System', Lecture Notes in Computer Science, vol. 8791, pp. 272-284. https://doi.org/10.1007/978-3-319-11397-5_21

APA

Pronoza, E., Yagunova, E., & Volskaya, S. (2014). Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System. Lecture Notes in Computer Science, 8791, 272-284. https://doi.org/10.1007/978-3-319-11397-5_21

Vancouver

Pronoza E, Yagunova E, Volskaya S. Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System. Lecture Notes in Computer Science. 2014;8791:272-284. https://doi.org/10.1007/978-3-319-11397-5_21

Author

Pronoza, E. ; Yagunova, E. ; Volskaya, S. / Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System. In: Lecture Notes in Computer Science. 2014 ; Vol. 8791. pp. 272-284.

BibTeX

@article{a3e7998e07e84d1a810a252da31e18ea,
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{\textquoteright}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.",
keywords = "Information extraction, Opinion mining, Restaurant recommendation system, Machine learning",
author = "E. Pronoza and E. Yagunova and S. Volskaya",
year = "2014",
doi = "10.1007/978-3-319-11397-5_21",
language = "English",
volume = "8791",
pages = "272--284",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Nature",

}

RIS

TY - JOUR

T1 - Corpus-Based Information Extraction and Opinion Mining for the Restaurant Recommendation System

AU - Pronoza, E.

AU - Yagunova, E.

AU - Volskaya, S.

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Information extraction

KW - Opinion mining

KW - Restaurant recommendation system

KW - Machine learning

U2 - 10.1007/978-3-319-11397-5_21

DO - 10.1007/978-3-319-11397-5_21

M3 - Article

VL - 8791

SP - 272

EP - 284

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -

ID: 5730060