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Social media data from financial websites contain information related to enterprise credit risk. Mining valuable new features in social media data helps to improve prediction performance. This paper proposes a credit risk prediction framework that integrates social media information to improve listed enterprise credit risk prediction in the supply chain.

The prediction framework includes four stages. First, social media information is obtained through web crawler technology. Second, text sentiment in social media information is mined through natural language processing. Third, text sentiment features are constructed. Finally, the new features are integrated with traditional features as input for models for credit risk prediction. This paper takes Chinese pharmaceutical enterprises as an example to test the prediction framework and obtain relevant management enlightenment.

The prediction framework can improve enterprise credit risk prediction performance. The prediction performance of text sentiment features in social media data is better than that of most traditional features. The time-weighted text sentiment feature has the best prediction performance in mining social media information.

The prediction framework is helpful for the credit decision-making of credit departments and the policy regulation of regulatory departments and is conducive to the sustainable development of enterprises.

The prediction framework can effectively mine social media information and obtain an excellent prediction effect of listed enterprise credit risk in the supply chain.

The purpose of this chapter is to shed light on researchers and practitioners about sentiment analysis in hospitality and tourism. The technical details described throughout the chapter with a case study to provide clarifying insights. The proposed chapter adds significantly to the body of text mining knowledge by combining a technical explanation with a relevant case study. The case study used supervised machine learning to predict overall star ratings based on 20,247 comments related to Royal Caribbean International services for determining the impact of cruise travel experiences on the evaluation company process. The results indicate that travelers evaluate their travel experiences according to the most intense negative or positive feelings they have about the company.

The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.

On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.

The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.

This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.

The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.

The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).

Online customer reviews could shed light into their experience, opinions, feelings, and concerns. To gain valuable knowledge about customers, it becomes increasingly important for businesses to collect, monitor, analyze, summarize, and visualize online customer reviews posted on social media platforms such as online forums. However, analyzing social media data is challenging due to the vast increase of social media data. The purpose of this paper is to present an approach of using natural language preprocessing, text mining and sentiment analysis techniques to analyze online customer reviews related to various hotels through a case study.

This paper presents a tested approach of using natural language preprocessing, text mining, and sentiment analysis techniques to analyze online textual content. The value of the proposed approach was demonstrated through a case study using online hotel reviews.

The study found that the overall review star rating correlates pretty well with the sentiment scores for both the title and the full content of the online customer review. The case study also revealed that both extremely satisfied and extremely dissatisfied hotel customers share a common interest in the five categories: food, location, rooms, service, and staff.

This study analyzed the online reviews from English-speaking hotel customers in China to understand their preferred hotel attributes, main concerns or demands. This study also provides a feasible approach and a case study as an example to help enterprises more effectively apply social media analytics in practice.

The purpose of this paper is to mine competitive intelligence in social media to find the market insight by comparing consumer opinions and sales performance of a business and one of its competitors by analyzing the public social media data.

An exploratory test using a multiple case study approach was used to compare two competing smartphone manufacturers. Opinion mining and sentiment analysis are conducted first, followed by further validation of results using statistical analysis. A total of 229,948 tweets mentioning the iPhone6 or the GalaxyS5 have been collected for four months following the release of the iPhone6; these have been analyzed using natural language processing, lexicon-based sentiment analysis, and purchase intention classification.

The analysis showed that social media data contain competitive intelligence. The volume of tweets revealed a significant gap between the market leader and one follower; the purchase intention data also reflected this gap, but to a less pronounced extent. In addition, the authors assessed whether social opinion could explain the sales performance gap between the competitors, and found that the social opinion gap was similar to the shipment gap.

This study compared the social media opinion and the shipment gap between two rival smart phones. A business can take the consumers’ opinions toward not only its own product but also toward the product of competitors through social media analytics. Furthermore, the business can predict market sales performance and estimate the gap with competing products. As a result, decision makers can adjust the market strategy rapidly and compensate the weakness contrasting with the rivals as well.

This paper’s main contribution is to demonstrat the competitive intelligence via the consumer opinion mining of social media data. Researchers, business analysts, and practitioners can adopt this method of social media analysis to achieve their objectives and to implement practical procedures for data collection, spam elimination, machine learning classification, sentiment analysis, feature categorization, and result visualization.

Given the maturation of the internet and virtual communities, an important emerging issue in the humanities and social sciences is how to accurately analyze the vast quantity of documents on public and social network websites. Therefore, this chapter integrates political blogs and news articles to develop a public mood dynamic prediction model for the stock market, while referencing the behavioral finance perspective and online political community characteristics. The goal of this chapter is to apply a big data and opinion mining approach to a sentiment analysis for the relationship between political status and economic development in Taiwan. The proposed model is verified using experimental datasets collected from ChinaTimes.com, cnYES.com, Yahoo stock market news, and Google stock market news, covering the period from January 1, 2016 to June 30, 2017. The empirical results indicate the accuracy rate with which the proposed model forecasts stock prices.

The 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.

A 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.

The study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate.

The 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.

The study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector.

This paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis.

The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.

This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.

The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.

Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry.

The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value.

The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management.

The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.

To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews.

Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper.

The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product.

This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms.

This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.