The purpose of this study is to employ a novel mixed method to better understand the differences in the customer service experience of the digital banking services in South Korea and the Philippines. Data mining techniques and customer journey mapping analysis were utilized to understand the proposed issues. The results indicate that there are four critical significant points of digital banking services between South Korea and the Philippines including the number of touchpoints, speed of results, registration requirements, and touchpoint deviations. Potential causes and implications are discussed in this article. The contribution of this study is using mixed approach to understand the issues which related to bank marketing in the digital era. Additionally, this study also enriches the investigations of customer service experience in banking across different countries. Overall, the findings of this study benefit the development of digital banking services, especially in the Asia Pacific countries.

Due to the rapidly growth of social media, potential moviegoers always depend on the online reviews to make their purchase decisions. Film companies need to know what aspects of reviews will drive sales up or down. This study proposes a framework which integrates an aspect-based sentiment analysis and econometric modeling to explore the relationships between the information features in online reviews and movie ticket sales. The empirical results indicate that whereas the rating does not matter to moviegoers and does not affect movie revenues, additional textual review, both positive and negative contents, does have a positive impact on moviegoers, and further prompts the movie revenues. These findings have significant implications for movie producers as well as advertisers to target promotions at their audience accordingly.

Recently, the rapid growth in the number of customer reviews on e-commence platforms and in the amount of user-generated content has begun to have a profound impact on customer purchasing decisions. To counter the negative impact of social media marketing, some firms have begun hiring people to generate fake reviews which either promote their own products or damage their competitor's reputation. This study proposes a framework, which takes advantage of both supervised and unsupervised learning techniques, for the observation of behaviors among spammers. Then, based on the behavior of participants on web forums, the authors build up a post-reply network. The main focus is on the behavior-related features of the reviews, their propagation, and their popularity. The primary objective of this study is to build an effective online spammer detection model and the method detailed in this work can be used to improve the performance of spammer detection models. An experiment is carried out with a real dataset, the results of which indicate that these new features are important for identifying spammers. Finally, random walk clustering is applied to investigate the post-reply network. Some interesting and important features are observed in the interactions between a group of spammers which could be subjected to further research.

Digital banks are new entrants in the banking industry in the Philippines as they only started late 2018. Since then, a handful of players have and are still emerging. With more and more people becoming technologically savvy, it is very critical for financial institutions to develop a digital banking application that will stand out from the competition. This paper aims to use text mining methods to analyse digital banking application reviews. This study will perform topic modelling using LDA to explore customer concerns and will mine association rules between the digital banking features with the review score. The results will reveal which areas the digital banking application can further optimize for customer satisfaction and retention.

The spread of information on Facebook and Twitter is much more efficient than on traditional social media platforms. For word-of-mouth (WOM) marketing, social media have become a rich information source for companies or scholars to design models to examine this repository and mine useful insights for marketing strategies. However, social media language is relatively short and contains special words and symbols. Most natural language processing (NLP) methods focus on processing formal sentences and are not well-suited to such short messages. In this study we propose a novel sentiment analysis framework based on deep learning models to extract sentiment from social media. We collect data from which we compile a dataset. After processing these special terms, we seek to establish a semantic dataset for further research. The extracted information will be useful for many future applications. The experimental data have been obtained by crawling several social media platforms.

This article describes how the bank industry in Taiwan must function in today's tough and fiercely competitive domestic credit card market and subdued global market. Banks are increasingly emphasizing the importance of retaining customers in order to sustain market share and remain profitable. This study proposes a new model which local banks can use to detect potential customer churn and provide an early warning indicator of problems that could lead to loss of customers. The model incorporates a customer relationship management database with a built-in time factor and applied temporal abstraction to represent data for a specific time period as defined by experts. Association rule mining is applied to analyze and detect abnormal customer behavior. The results of this article indicate that the system is relatively effective in detecting customer churn early on and thus helpful at assisting banks to address issues before they escalate. Furthermore, the tested rules are further scrutinized by experts to establish the relationship between the defined rules and management. This study provides an expert system for banks to assess the quality of their marketing campaigns and reestablish faltering customer relationships.

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. This paper proposes an attention-based long short-term memory model to predict stock price movement and make trading strategies.

The massive amounts of data available on social media platforms become the key source of information related to customer sentiment and opinions for analysis by companies. Facebook is one of the most popular platforms which a firm can take advantage of to establish customer relationships through the use of fan pages. Facebook fan pages contain rich sources of information related to customer relationships. This study proposes a novel recommender system based on analyzing the behavior of fans. The proposed method is able to overcome the limitations of traditional recommender system. Three common measures will be used to evaluate the effectiveness of the proposed method. The experimental data have been obtained by crawling from IMDb fan pages..

Online customer reviews of both products and merchants can greatly affect the readers' purchase decision making. Opinion spamming refers to the writing of fake reviews that try to deliberately mislead the reader by giving undeserved positive opinions or unjust or false negative opinions to promote or demote the target product. This paper focuses on analyzing spammer behaviors on a well-known Taiwan web forum, Mobile01. We explore the characteristics of the spam and the spammers operating within such a web forum to obtain some insights for future study.