Sentiment Analysis of Smartphone Accounting Application Users

The focus of this research is to summarize the reviews conducted by accounting application users to explore what aspects they like about the accounting application. This research uses review sentences with a total of 4923 review sentences on Google and Apple platforms. The review mining method used in this study implements the Feature-Based Summarization (FBS). The conclusion of this study is that there are six product features that are preferred by accounting application users. The product features are reports, transactions, bookkeeping, profit, category, and customers. This research has explored product features in accounting applications, but not all product features are discussed by users. Therefore, the discussion on review sentences focuses on the six product features. This study is able to provide practical recommendations to Small-Medium Enterprises (SMEs) actors in making smartphone-based application decisions they will use. This study recommends SMEs to use accounting applications with the above product features. This is because the strong discussion of opinions on product features explains the preference for product features for actors in helping them prepare financial reports. As qualitative research, this study does not have the ability to generalize the results of the study to a population.

Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges

The increasing number of product reviews posted online is a gold mine for designers to know better about the products they develop, by capturing the voice of customers, and to improve these products accordingly. In the meantime, product design and development have an essential role in creating a more sustainable future. With the recent advance of artificial intelligence techniques in the field of natural language processing, this research aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. In this paper, the opportunities and challenges offered by existing frameworks — including Python libraries, packages, as well as state-of-the-art algorithms like BERT — are discussed, illustrated, and positioned along an ad hoc machine learning process. This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products, including the five following stages, from the identification of sustainability-related reviews to the interpretation of sustainable design leads: data collection, data formatting, model training, model evaluation, and model deployment. Examples of sustainable design insights that can be produced out of product review mining and processing are given. Finally, promising lines for future research in the field are provided, including case studies putting in parallel standard products with their sustainable alternatives, to compare the features valued by customers and to generate in fine relevant sustainable design leads.

Exploring energy-saving refrigerators through online e-commerce reviews: an augmented mining model based on machine learning methods

PurposeBased on climate issues and carbon emissions, this study aims to promote low-carbon consumption and compel consumers to actively shift to energy-saving appliances. In this big data era, online reviews in social and electronic commerce (e-commerce) websites contain valuable product information, which can facilitate firm business strategies and consumer comparison shopping. This study is designed to advance existing research on energy-saving refrigerators by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions to e-commerce platform managers and manufacturers to effectively understand the psychological cognition of consumers.Design/methodology/approachThis study proposes an online e-commerce review mining and management strategy model based on “data acquisition and cleaning, data mining and analysis and strategy formation” through multiple machine learning methods, namely, Bayes networks, support vector machine (SVM), latent Dirichlet allocation (LDA) and importance–performance analysis (IPA), to help managers.FindingsBased on a case study of one of the largest e-commerce platforms in China, this study linguistically analyzes 29,216 online reviews of energy-saving refrigerators. Results indicate that the energy-saving refrigerator features that consumers are generally satisfied with are, in sequential order, logistics, function, price, outlook, after-sales service, brand, quality and space. This study also identifies ten topics with 100 keywords by analyzing 18 different refrigerator models. Finally, based on the IPA, this study allocates different priorities to the features and provides suggestions from the perspective of consumers, the government and manufacturers.Research limitations/implicationsIn terms of limitations, future research may focus on the following points. First, the topics identified in this study derive from specific points in time and reviews; thus, the topics may change with the text data. A machine learning-based online review analysis platform could be developed in the future to dynamically improve consumer satisfaction. Moreover, given that consumers' needs may change over time, e-commerce platform types and consumer characteristics, such as user profiles, can be incorporated into the model to effectively analyze trends in consumers' perceived dimensions.Originality/valueThis study fills the gap in previous research in this field, which uses small-sample data for qualitative analysis, while integrating management ideas and proposes an online e-commerce review mining and management strategy model based on machine learning methods. Moreover, this study considers how consumers' emotional and thematic preferences for products affect their purchase decision-making from the perspective of their psychological perception and linguistically analyzes online reviews of energy-saving refrigerators using the proposed mining model. Through the improved IPA model, this study provides optimizing strategies to help e-commerce platform managers and manufacturers.

Voice of urban park visitors: exploring destination attributes influencing behavioural intentions through online review mining

In this paper, we will identify the destination attributes of a popular urban park and investigate their specific roles in forming visitors' behavioural intentions using text mining approaches. The principles of natural language processing and psychometric procedure were combined to achieve the objectives of the research. Initially, park visitors’ online reviews were collected and analysed to identify possible latent dimensions for questionnaire design. Then, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used for crucial factor selection and verification. Lastly, a structural equation model (SEM) was constructed to investigate the impacts of these park attributes on the behavioural intention of visitors.