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Determining banking service attributes from online reviews: text mining and sentiment analysis

PurposeThe current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors.Design/methodology/approachA total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis.FindingsThe study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.Research limitations/implicationsThe study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings.Practical implicationsThe study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.Originality/valueThis paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

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Management Response to Online Review: The Case of Hong Kong Luxury Hotels

Electronic word-of-mouth (eWOM) is regarded as crucial in business development. Given the intangible nature of tourism and hospitality products, potential customers find it hard to assess them before making purchase. Accordingly, online customer reviews and management responses have influential roles in their decision-making process. While a plethora of previous research focused on customer reviews, scholarly attention on how luxury hotels respond to the reviews was scant. Using content analysis, this study examines the management response characteristics of 35 luxury hotels and response style of 7 luxury chain hotels in Hong Kong. Their response characteristics including response frequency, responder’s job position, and timeliness of response were generally similar. The response style and tone (professional and conversational tones) vary with hotels even they are in the same hotel group. Implications on practice of management responses are offered for luxury hotel operators.

Study of E-Commerce Customer Review Capturing Process in the Indian Context

Online customer reviews provided by customers on e-commerce sites who had bought the products proved to be a key parameter. New and potential customers at the pre-purchase stage to vet the merits and demerits before buying new products listed on e-commerce sites referred to online customer reviews. However, there have been very few studies that focused on online customer review capturing process. Thus, this research work focused on the review capturing process of e-commerce websites from a customer's point of view to understand the online customer review process. A qualitative exploratory research was carried out. An open-ended semi-structured questionnaire was used to understand customer's stand on the e-commerce review capturing process. In-depth interviews were collected from customers. The data was analyzed thematic content. The study findings indicated what motivated customers to write online reviews, what inhibited them from writing reviews and what were their suggestions for the managers of e-commerce organizations towards designing better online review capturing.

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

Assessing technology adoption practices in Chinese theme parks: text mining and sentiment analysis

Purpose This study aims to examine technology adoption practices in Chinese theme parks by leveraging text mining and sentiment analysis approaches on actual theme park customers’ online reviews. Design/methodology/approach The study text mined a total of 65,518 reviews of 490 Chinese theme parks with the aid of the Python program. Further, it computed sentiment scores of the customer reviews associated with the ratings of each categorized technology practice applied in the theme parks. Findings The study identified two major categories of technology applications in theme parks: supporting and experiential technologies. Multiple statistical tests confirmed that supporting technologies consisted of three types: intelligent services, ticketing and in-park transportation. Experiential technologies further included five aspects of technologies according to Schmitt’s strategic experiential modules (SEMs): sense, feel, act, think and relate. Originality/value The study findings contribute to the current understanding of theme park visitors’ perceptions of technology adoption practices and provide insightful implications for theme park practitioners who intend to invest in high technology solutions to deliver a better customer experience.