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

Abstract

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. IntroductionIn this paper information extraction (IE) method for the Russian restaurant recommendation system is proposed. It is based on the application of linguistic information gathered from corpus analysis and can be used for similar domains and underresourced languages. Our information extraction framework is a part of the project which aims at implementing restaurants recommendation system, and in this paper we consider two tasks: reviews corpus analysis and the application of machine learning techniques to the problem in question. During the latter task we use the information obtained at the corpus analysis phase. Our approach includes opinion mining since restaurant characteristics are both objective and subjective. Our corpus analysis method is based on non-contiguous bigrams and part of speech (POS) distribution analysis. Trigger words dictionaries are learnt using the bootstrapping method. E. Pronoza et al.The frames to be extracted include service quality, food quality, cuisine type, price level, noise level, etc. Each frame has its own set of aspects. We suppose that the most important characteristics of a restaurant are service and food quality and cuisine type and therefore we only consider these three frames and focus on the extraction of their aspects. Such an assumption is proved by the distribution of the aspects in the data.We also suppose that the proposed IE system can be highly effective despite the difficulties imposed by the structure of a typical Russian restaurant review. Although the key information about restaurant characteristics does not always lie on the surface, tuning machine learning models according to the results of corpus analysis can help to improve the performance of an IE system. Related WorkInformation extraction (IE) task as part of recommendation system development is discussed in [21]. The authors propose a rule-based approach to the extraction of key words from user's email. These keywords are put in...

Recommender System for Tourist Itineraries Based on Aspects Extraction from Reviews Corpora

“…It should be also stated that including emoticons and exclamations into the feature set is not a good idea unless the aspect is service quality. For the other aspects it does not improve F1 or even impairs it [28].…”

“…The predicative-attributive dictionaries were chosen, in particular for adjectives and full and short participles, which refer to nominations of the key frames. This is conditioned by the POS distribution analysis within corpus n-grams showed dominating of noun phrases in most aspects description [28].…”

“…The approach towards corpora analysis presented in this paper is based on non-contiguous bigrams and part of speech (POS) distribution analysis [28]. Trigger words dictionaries are obtained by means of the bootstrapping method.…”

“…The approach for patterns construction presented in this paper is based on ngrams (n ranges from 1 to 8) and POS-distribution analysis. Trigger words dictionary and predicative-attributive dictionaries are obtained by means of the bootstrapping method, targeted at the aspects of interest [27], [28].…”

Restaurant Review Analysis using Natural Language Processing

“…They showed that their model achieved an accuracy almost like offline methods. Ekaterina Pronoza [6] et.al projected a eating house info extraction technique for the eating house recommendation system. Veda Waikul [7] et.al projected an SVM classifier for classifying restaurant reviews.…”