Aspect-Based Restaurant Information Extraction for the Recommendation System

Pronoza, Ekaterina V.; Yagunova, Elena; Volskaya, Svetlana

doi:10.1007/978-3-319-43808-5_28

Cited by 9 publications

(10 citation statements)

References 15 publications

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“…Among four extracted fundamental dining aspects, taste/food, value, and experience is consistent with prior studies that apply text-mining analysis to discover hidden restaurant aspects in online reviews [73][74][75][76]. However, location is barely mentioned by previous studies.…”

Section: Summary Of Results and Discussionsupporting

confidence: 84%

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Luo

2019

Sustainability

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Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naïve Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers’ needs and maintain sustainable competitive advantages.

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Section: Summary Of Results and Discussionsupporting

confidence: 84%

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Luo

2019

Sustainability

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“…This stage (as well as the other stages) of corpus analysis is described in [25] and therefore we shall not go into details here, only paying attention to the key issues. All trigger words dictionaries described in this paper (each of them consisting of a set of lexemes with a given POS) are constructed using the bootstrapping method.…”

Section: Dictionary Constructionmentioning

confidence: 99%

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

Pronoza

Yagunova

Volskaya

et al. 2014

Lecture Notes in Computer Science

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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...

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“…The first approach implies using templates and semantic resources (e.g. Word-Net-Affect, SentiWordNet, SenticNet), while statistical methods allow solving the task without such resources [27]. For recommender systems, in particular for museums and conterminal fields, three approaches are mostly combined: (1) content-based, (2) aspect-based, and (3) user-based [17], [21], [30], [32].…”

Section: Aspect-based Restaurant and Museum Information Extractionmentioning

confidence: 99%

“…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].…”

Section: B Museums Information Extractionmentioning

confidence: 99%

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

Volkova¹,

Yagunova²,

Pronoza³

et al. 2018

Polibits

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In this paper a recommender system is described which takes a set of venue categories of user's interest into account to form a tourist itinerary throughout a city. The system is focused on user preferences in venue aspects. Techniques of such aspects extraction are developed in this paper, in particular from reviews corpora. User preferences are used to weigh aspects associated with particular sights and restaurants. These filtered venues along with time restrictions are subject to submit into the recommender system. A lightweight ontology is discussed which describes the domains of restaurants and sightseeing knowledge and allows venues comparative analysis to enhance the search for relevant venues. The system designed performs automated planning of tourist itineraries, flexible sights searching, and analysis of venues aspects extracted from reviews in Russian.

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Aspect-Based Restaurant Information Extraction for the Recommendation System

Cited by 9 publications

References 15 publications

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

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

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

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