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Restaurant Information Extraction (Including Opinion Mining Elements) for the Recommendation System. / Pronoza, Ekaterina; Yagunova, Elena; Volskaya, Svetlana; Lyashin, Andrey.

In: Lecture Notes in Computer Science, Vol. 8856, No. 8856, 2014, p. 201-220.

Research output: Contribution to journal › Article

Harvard

Pronoza, E, Yagunova, E, Volskaya, S & Lyashin, A 2014, 'Restaurant Information Extraction (Including Opinion Mining Elements) for the Recommendation System', Lecture Notes in Computer Science, vol. 8856, no. 8856, pp. 201-220. https://doi.org/10.1007/978-3-319-13647-9

APA

Pronoza, E., Yagunova, E., Volskaya, S., & Lyashin, A. (2014). Restaurant Information Extraction (Including Opinion Mining Elements) for the Recommendation System. Lecture Notes in Computer Science, 8856(8856), 201-220. https://doi.org/10.1007/978-3-319-13647-9

Vancouver

Pronoza E, Yagunova E, Volskaya S, Lyashin A. Restaurant Information Extraction (Including Opinion Mining Elements) for the Recommendation System. Lecture Notes in Computer Science. 2014;8856(8856):201-220. https://doi.org/10.1007/978-3-319-13647-9

Author

Pronoza, Ekaterina ; Yagunova, Elena ; Volskaya, Svetlana ; Lyashin, Andrey. / Restaurant Information Extraction (Including Opinion Mining Elements) for the Recommendation System. In: Lecture Notes in Computer Science. 2014 ; Vol. 8856, No. 8856. pp. 201-220.

BibTeX

@article{8b0684398c624c9bbff6efd44131080c,
title = "Restaurant Information Extraction (Including Opinion Mining Elements) for the Recommendation System",
abstract = "In this paper information extraction method for the restaurant recommendation system is proposed. We aim at the development of an information extraction (IE) system which is intended to be a module of the recommendation system. The IE system is to gather information about different aspects of restaurants from online reviews, structure it and feed the recommendation module with the obtained data. The analyzed frames include service and food quality, cuisine, price level, noise level, etc. In this paper service quality, cuisine type and food quality are considered. As part of corpus preprocessing phase, a method for Russian reviews corpus analysis (as part of information extraction) is proposed. Its importance is shown at the experimental phase, when the application of machine learning techniques to aspects extraction is analyzed. It is shown that the ideas obtained at the corpus preprocessing stage can help to improve machine learning models performance.",
keywords = "corpus analysis, restaurant reviews, information extraction, recommendation system, machine learning",
author = "Ekaterina Pronoza and Elena Yagunova and Svetlana Volskaya and Andrey Lyashin",
year = "2014",
doi = "10.1007/978-3-319-13647-9",
language = "English",
volume = "8856",
pages = "201--220",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Nature",
number = "8856",

}

RIS

TY - JOUR

T1 - Restaurant Information Extraction (Including Opinion Mining Elements) for the Recommendation System

AU - Pronoza, Ekaterina

AU - Yagunova, Elena

AU - Volskaya, Svetlana

AU - Lyashin, Andrey

PY - 2014

Y1 - 2014

N2 - In this paper information extraction method for the restaurant recommendation system is proposed. We aim at the development of an information extraction (IE) system which is intended to be a module of the recommendation system. The IE system is to gather information about different aspects of restaurants from online reviews, structure it and feed the recommendation module with the obtained data. The analyzed frames include service and food quality, cuisine, price level, noise level, etc. In this paper service quality, cuisine type and food quality are considered. As part of corpus preprocessing phase, a method for Russian reviews corpus analysis (as part of information extraction) is proposed. Its importance is shown at the experimental phase, when the application of machine learning techniques to aspects extraction is analyzed. It is shown that the ideas obtained at the corpus preprocessing stage can help to improve machine learning models performance.

AB - In this paper information extraction method for the restaurant recommendation system is proposed. We aim at the development of an information extraction (IE) system which is intended to be a module of the recommendation system. The IE system is to gather information about different aspects of restaurants from online reviews, structure it and feed the recommendation module with the obtained data. The analyzed frames include service and food quality, cuisine, price level, noise level, etc. In this paper service quality, cuisine type and food quality are considered. As part of corpus preprocessing phase, a method for Russian reviews corpus analysis (as part of information extraction) is proposed. Its importance is shown at the experimental phase, when the application of machine learning techniques to aspects extraction is analyzed. It is shown that the ideas obtained at the corpus preprocessing stage can help to improve machine learning models performance.

KW - corpus analysis

KW - restaurant reviews

KW - information extraction

KW - recommendation system

KW - machine learning

U2 - 10.1007/978-3-319-13647-9

DO - 10.1007/978-3-319-13647-9

M3 - Article

VL - 8856

SP - 201

EP - 220

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

IS - 8856

ER -

ID: 5732817