Research output: Contribution to journal › Article
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
}
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