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 -