Armina Mohseni; ameneh khadivar; Fatemeh Abbasi
Abstract
The growth of the Internet, social networks and e-commerce websites provide a platform for users to express their opinions. In recent years, many users have expressed their positive or negative opinions about food, service, and quality and restaurant atmosphere online. These comments are very important ...
Read More
The growth of the Internet, social networks and e-commerce websites provide a platform for users to express their opinions. In recent years, many users have expressed their positive or negative opinions about food, service, and quality and restaurant atmosphere online. These comments are very important for the decision of other users as well as restaurants to maintain quality, product development and their brand. Sentiment analysis is a natural language processing approach and allows systematic analysis of users' opinions. Due to the importance of this issue, the purpose of this study is to present a model for analyzing the sentiment of TripAdvisor's comments about Iranian restaurants. In this research, we propose an aspect-based sentiment analysis based on a deep learning algorithm which is the standard long short-term memory neural network to extract users' sentiments about restaurants. To teach the model, 4000 comments were labeled according to four aspects in three classes of not related, positive and negative, and the study steps were done based on Crisp methodology. Accuracy for food, service, value and atmosphere were 82%, 86%, 87% and 81%, respectively. These results indicate the efficiency and acceptable performance of the model for aspect-based sentiment analysis of restaurants. Furthermore, food and atmosphere are the most important aspects for the customers of Iranian restaurants, respectively. Restaurant owners can use the developed model to gain a competitive advantage and find their strengths and weaknesses.
Mohammad’reza Gholamian; Azimeh Mozafari
Abstract
Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree ...
Read More
Management and evaluation of valuable customers, is one of the most important banking factors to reduce costs and increase profitability. In recent decades, many researchers have studied on the analysis of the customer attributes to evaluate value of them using data mining techniques and decision tree is one of the most widely used data mining algorithms in the field. Since this algorithm for built tree, considers only one attribute at a time to test each node and ignores the dependency between attributes, therefore, required maximum memory is increased. To solve this problem, in this research a method is proposed to improve the decision tree using neural network to explore the dependency between the attributes based on reduction in required maximum memory that is used based on RFM model to predict customer values. Results show that the proposed method using dependencies between attributes will predict the new customer values by less maximum memory compare to the basic method
