Research output: Contribution to journal › Article
Aspect-Based Restaurant Information Extraction for the Recommendation System В книге "Human Language Technology. Challenges for Computer Science and Linguistics. 6th Language and Technology Conference, LTC 2013, Poznań, Poland, December 7-9, 2013. Revised Selected Papers" Number of Pages: XVII, 422. / Pronoza, Ekaterina; Yagunova, Elena; Volskaya, Svetlana.
In: Lecture Notes in Computer Science, Vol. Volume 9561, 2016, p. pp 371-385.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Aspect-Based Restaurant Information Extraction for the Recommendation System В книге "Human Language Technology. Challenges for Computer Science and Linguistics. 6th Language and Technology Conference, LTC 2013, Poznań, Poland, December 7-9, 2013. Revised Selected Papers" Number of Pages: XVII, 422
AU - Pronoza, Ekaterina
AU - Yagunova, Elena
AU - Volskaya, Svetlana
PY - 2016
Y1 - 2016
N2 - In this paper information extraction task for the restaurant recommendation system is considered. We develop an information extraction system which is intended to gather restaurants aspects from users’ reviews and output them to the recommendation module. As many of the restaurant aspects are subjective, our task can also be called sentiment analysis, or opinion mining. Thus, we present an aspect-based approach towards sentiment analysis of reviews about restaurants for e-tourism recommender systems. The analyzed frames are service and food quality, cuisine, price level, noise level, etc. In this paper we focus on service quality, cuisine type and food quality. As part of the 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 information obtained during corpus analysis improve system perfor
AB - In this paper information extraction task for the restaurant recommendation system is considered. We develop an information extraction system which is intended to gather restaurants aspects from users’ reviews and output them to the recommendation module. As many of the restaurant aspects are subjective, our task can also be called sentiment analysis, or opinion mining. Thus, we present an aspect-based approach towards sentiment analysis of reviews about restaurants for e-tourism recommender systems. The analyzed frames are service and food quality, cuisine, price level, noise level, etc. In this paper we focus on service quality, cuisine type and food quality. As part of the 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 information obtained during corpus analysis improve system perfor
KW - Corpus analysis Restaurant reviews Aspect-based information extraction Recommendation system Machine learning E-tourism Sentiment analysis Opinion mining
U2 - 10.1007/978-3-319-43808-5_28
DO - 10.1007/978-3-319-43808-5_28
M3 - Article
VL - Volume 9561
SP - 371
EP - 385
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -
ID: 7633603