maryam zolfaghar; ameneh khadivar; fatemeh abbasi
Abstract
Technology progress, during the recent decades, has influenced the banking industry. Banks have moved from traditional banking to online banking. Users expect to view systems on different browsers or different devices without disrupting the overall/page due to the small size of the display on some devices ...
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Technology progress, during the recent decades, has influenced the banking industry. Banks have moved from traditional banking to online banking. Users expect to view systems on different browsers or different devices without disrupting the overall/page due to the small size of the display on some devices such. This expectation is responsiveness which is one of the factors influencing the user experience. Responsiveness means that the contents of a web-based system are displayed correctly and clearly on all devices and browsers, regardless of screen size. The purpose of this article is also to design a responsive Internet Banking system based on design science. It has been designed, based on the study of the scientific background and considering the concepts of Internet Banking, its adoption factors, responsiveness and user experience and the system requirements, from a business and technical point of view.The present study provides a system design that can be used by all financial institutions. It can also be applicable for universities and higher education institutions, as it describes, step by step, how to practically use the research method in a real system.Considering the results of this study, user experience, including issues such as responsiveness, ease of use and learnability, is much interesting for users, and the Internet banks that provide these factors along with security and performance are very attractive and are highly appreciated.
Fatemeh Abbasi; Ameneh Khadivar; Mohsen Yazdinejad
Abstract
Nowadays, people use others' opinions on social networks for decision-making to purchase online products and services. Likewise, the companies which offer the products employ sentiment analysis of opinions of users and customers to adopt informed decisions and offer new products. Considering the ...
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Nowadays, people use others' opinions on social networks for decision-making to purchase online products and services. Likewise, the companies which offer the products employ sentiment analysis of opinions of users and customers to adopt informed decisions and offer new products. Considering the high volume of the contextual data, conversion, and analysis of such data is a major challenge in e-commerce. Sentiment analysis is a modern approach in the extraction of opinions. The obtained information from sentiment analysis can have a considerable impact on the efficient selection of customers. In the present study, a model has been proposed for sentiment analysis of users' opinions for buying a cell phone in Digikala. This study is applicable to the objective aspect. The data includes users' opinions in Digikala. The statistical sample consists of opinions of cell phone users in Digikala. Supervised learning, as well as Python package, were utilized for analysis and implementation. A model has been proposed for sentiment analysis of users' opinions. The results demonstrate that this model can classify users' opinions with an accuracy equal to 0.892. Similarly, the results reveal that users' opinions about ease of use, possibilities, and capabilities of the cell phone are positive and about purchase value to price, innovation, design and appearance, and quality of cell phones are negative. The proposed model can be implemented in e-commerce websites like Digikala and its output can be observed by users systematically. Finally, it can be led to inform decision-making for buyers and companies which offer products.
Fatemeh Abbasi; Babak Sohrabi; Amir Manian; Ameneh Khadivar
Abstract
In recent years, the growth of social networks and, consequently, the increasing content of these networks have led people to use others’ opinions to make decisions for the purchase and use of products, services or even political choices. Given the fact that users' comments are textual and ...
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In recent years, the growth of social networks and, consequently, the increasing content of these networks have led people to use others’ opinions to make decisions for the purchase and use of products, services or even political choices. Given the fact that users' comments are textual and their reading and summarizing is timely and difficult, the automation of the extraction of opinions and sentiments of users' comments is one of the suggested solutions for online sales sites to provide more efficient services to customers for better decision making. Sentiment analysis or opinion mining is a process where people's opinions, feelings and attitudes are extracted in relation to a particular subject and are recognized as a branch of the text mining. The results of sentiment analysis can be used in recommender systems to provide more effective shopping suggestions. Information derived from the opinion mining can be used in a variety of fields such as libraries for better choices and purchases based on the users' real opinions. In this research, a system for automatically categorizing the sentiments expressed in the opinions of the buyers of the Amazon book website is presented. The system is designed using ensemble voting models to analyze the sentiment of Amazon users' comments. For all analyses, Python text mining packages are used. In ensemble method two methods are used: majority voting and weight-based voting. In the weighting method, a greater weight is assigned to a classifier by higher accuracy. By comparing the performance of the results, the weighting model is chosen as the final model for making the sentiment analysis. Results show that the proposed system can automatically classify positive and negative comments with an accuracy of over 80%.
