This study represents an efficient method for extracting product aspects from customer reviews and give solutions for inferring aspect ratings and aspect weights. Aspect ratings often reflect the user's satisfaction on aspects of a product and aspect weights reflect the degree of importance of the aspects posed by the user. These tasks therefore play a very important role for manufacturers to better understand their customers' opinion on their products and services. The study addresses the problem of aspect extraction by using aspect words based on conditional probability combined with the bootstrap technique. To infer the user's rating for aspects, a supervised approach called the Naïve Bayes classification method is proposed to learn the aspect ratings in which sentiment words are considered as features. The weight of an aspect is estimated by leveraging the frequencies of aspect words within each review and the aspect consistency across all reviews. Experimental results show that the proposed method obtains very good performance on real world datasets in comparison with other state-of-the-art methods.
Abstract-Sentence compression is a valuable task in the framework of text summarization. In previous works, the sentence is reduced by removing redundant words or phrases from original sentence and tries to remain information. In this paper, we propose a new method that used Grid Model and dynamic programming to calculate n-grams for generating the best sentence compression.These reduced sentences are combined to text summarization. The experimental results showed that our method really effective and the text is grammatically, coherence and concise.
Measuring customer satisfaction is a key task for hotels today. Analyzing online reviews of experienced guests will help the managers to know if guests are satisfied or dissatisfied with the service that they provided. Hence, they have solutions to improve service quality. This study presents a method to measure guest satisfaction based on sentiment lexicon that is developed for hospitality domain to increase the accuracy of the analysis results. Actual data is downloaded from TripAdvisor with 35 four-star to five-star hotels of five cities in Vietnam to analyze guest satisfaction that shows that nearly 80% of customers are satisfied with the quality of Vietnamese hotels. Based on data analysis, the study also evaluating guest loyalty through phrases like “came here several,” “come back,” “recommend,” etc. This rate corresponds to the number that was reported by the Vietnam National Administration of Tourism.
The World Wide Web has brought us a vast amount of online information. When we search with a keyword, data feedback from many different websites and the user cannot read all the information. So that, text summarization has become a hot topic, it has attracted experts in data mining and natural language processing field. For Vietnamese, some methods of text summarization based on that have been proposed for English also bring some significant results. However, still remain some difficult problems to treat with the Vietnamese language processing, typical in this is the Vietnamese text segmentation tool and text summarization corpus. In this paper, we present a Vietnamese text summarization method based on sentence extraction approach using neural network for learning combine reducing dimensional features to overcome the cost when building term sets and reduce the computational complexity. The experimental results show that our method is really effective in reducing computational complexity, and is better than some methods that have been proposed previous.
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